New Inquiring Lines

This explores why methods that let a model spend extra compute at inference — sampling many candidates, then searching and combining them — reward a model for producing varied competent answers rather than collapsing onto its single most-likely one.

This reads as a question about a specific training trick — alternating learning rate schedules pulling against weight decay to keep a model's outputs varied — but the corpus doesn't hold that exact mechanism, so the honest answer maps the adjacent territory it does cover: how cyclic training dynamics and diversity-preservation interact.

This explores whether a model can learn — rather than hand-tune — how to pick which 'experts' or ensemble members to fire and how heavily to weight each one given a compute budget, and whether the corpus has anything on that learned routing under its various names.

This explores the division of labor between two retrieval styles — dense (embedding-based, matching by meaning) and exact-match (lexical, matching literal strings) — and asks which kinds of queries actually reward semantic matching over literal overlap.

This explores whether you can take a committee of models (or many sampled predictions) that beats any single model, and compress that gain back into one model you actually ship — and the corpus suggests the real question is *what* you'd be distilling, because the ensemble advantage often isn't where people assume.

This explores when you should swap embedding-based retrieval (rank documents by vector similarity) for an agent that searches by issuing readable, executable commands like grep — and what the corpus says about which jobs each is actually good at.

This reads the question as asking how external supports — diverse demonstrations, structured feedback, differential trajectory handling, memory — can stabilize the parts of reinforcement learning that break down when you train models to search.

This reads the question as asking whether inference-time methods that act at the decoder — steering outputs, editing internal representations, composing skills on the fly — can stand in for both retrieval (search) and weight fine-tuning (parameter updates), and where that substitution breaks.

This explores the implicit human capacities — verification, social judgment, and contextual sense-making — that AI systems quietly assume the reader will supply, rather than the systems themselves providing them.

This explores the tradeoffs between three ways of getting new knowledge into a system — training a dedicated memory model, retrieval (RAG), and fine-tuning — and where the memory-model route actually wins.

This explores how breaking a complex question into smaller sub-queries can cut the compute spent on retrieval at inference time — and what the corpus says about when that pays off.

This explores why dense vector embeddings — the standard retrieval workhorse — blur together distinct entities that should stay separate, and what the corpus says is causing it.

This explores why agents shine on tasks that match patterns they've already seen but stumble on genuinely novel work — and the corpus suggests the answer is less about raw intelligence than about where competence comes from: demonstrated routines vs. open-ended exploration.

This explores 'memory' in the broad sense — how a model holds and recalls a growing body of facts or context — and asks where the ceiling is: is it the parameters, the context window, or something else entirely?

This explores why AI generation currently outruns verification, and the architectural tricks — asynchronous checking, reusable formal verifiers, and process-level inspection — that could invert that asymmetry so checking an artifact costs less than producing it.

This explores whether making failed experiments and abandoned approaches into publishable artifacts—rather than editorial waste—could shift the incentives that currently push researchers to hide the messy parts of how work actually happened.

This explores how an agent learning from its own experience can tell the difference between a lesson it should generalize broadly and a rare, situation-specific fact worth keeping intact — the corpus mostly attacks this as a question of *how much to compress* a stored memory.

This explores whether you can credit a measured reasoning gain to the actual method when you don't know what's doing the work — the training optimizer (SFT, RL) versus the inference-time scaffold (prompts, decoding tricks, abstractions) — and the corpus says attribution is genuinely hard because the headline metric often hides where the gain came from.

This explores the eviction problem in agent memory and context systems — how to decide what to throw away so you don't keep dead weight, yet never delete the rare item that turns out to matter — and the corpus suggests the answer is less a 'policy' than an architecture choice about who decides and how.

This explores how three distinct decay modes—old facts that no longer hold (staleness), quietly accumulating distortion (drift), and bad material polluting the store (contamination)—each break agent memory through different mechanisms, and what the corpus says about countering each.

This explores what the verifier — the thing that judges whether reasoning is correct — actually contributes to a model getting better at reasoning, and whether its design (or even its presence) is what drives the gains.

This explores whether agents reason better when they pull in only the relevant slices of their past (memory, retrieved facts, prior steps) versus stuffing everything they've seen into the prompt — and the corpus comes down firmly on the side of selectivity.

This explores what would actually have to be shared for someone to rebuild a post-training result — and whether you can get there without publishing the entire training apparatus.

This explores whether there's a 'good enough' detection rate that defeats attacks where false evidence is dropped into a model's context (RAG documents, agent messages, web content) — and the corpus answer is that the attack economics, not a detection percentage, are the real lever.

This explores why feeding identical data to different models yields uneven improvements — what about a model's starting point, scale, or current ability changes what the same examples teach it.

This explores whether—and how—LLMs actually absorb information you put in front of them (in a prompt or mid-conversation) into a jointly held 'we both know this now' ground, versus treating it as something less binding.

This explores why transformer attention is built to weight whatever is prominent in its context window — rather than to check whether that context is actually true — and what in the architecture makes that the default.

This explores whether one planted falsehood — a fake citation, a fabricated authority — can move a model's stated beliefs as forcefully as a sustained back-and-forth where a user keeps pushing, and the corpus suggests the two attack the model through different doors.

This reads the question as: where do AI agents still hit a wall in real work, and which of those walls are specifically about needing a human in the loop rather than more model horsepower.

This explores why AI systems meant to persist and accumulate experience across many tasks need a different architecture than tools spun up to help with a single request — and what specifically changes when continuity becomes the design goal.

This explores 'soft thinking' — reasoning in continuous concept space where the model carries probability-weighted blends of tokens forward instead of committing to one discrete word — and whether sampling in that continuous space can produce useful exploration without any added training, which the corpus mostly treats as a cautionary tale.

This explores whether agents that store and reuse the routines they discover — 'workflow memory' — actually post measurable performance gains, and what makes that compounding work.

This explores whether 'thinking by iterative refinement at inference time' — energy minimization, recursion, diffusion-style denoising — can match the reasoning gains of standard approaches, rather than only the variational/probabilistic methods usually framed this way.

This explores why agents lose the thread over long, multi-turn tasks — and why a small, rule-governed 'committed state' (what the agent has actually locked in) holds up better than replaying the whole conversation transcript.

This explores the gap between what a model *appears* to do at the surface — its output formats, behaviors, reasoning moves — and the training dynamics that actually installed those behaviors, and why the two are easy to confuse.

This explores why training a model on reasoning traces full of wrong or irrelevant steps can match — and occasionally beat — training on correct ones, especially on problems unlike those it was trained on.

This explores whether AI evaluation should move away from clean, single-score contests (leaderboards, one-shot benchmarks) and toward the kind of long, ambiguous, multi-step work real systems actually do — and what the corpus says is gained or lost in that shift.

This explores what a single overall score (the percentage of tasks an AI gets right) flattens out — the separate, often conflicting capabilities that hide underneath one number.

This explores whether fixed, rule-based scoring — single benchmark numbers, exact-match grading, temperature-zero determinism — can substitute for the messier judgment that real-world deployment demands, and where the corpus says that substitution breaks.

This explores why benchmark scores climb to ceiling fast — and the corpus suggests the cause is less about models getting smarter overnight than about what benchmarks accidentally reward: contamination, narrow task design, and optimization pressure on the exact thing being measured.

This explores whether models that are bad at following instructions can be fixed through targeted training — and what the corpus reveals about why naive instruction tuning often doesn't deliver real instruction-following.

This explores how to split a fixed model budget between the work of evolving/updating a harness (writing the protocols, skills, memory edits) and the work of actually using that harness to do tasks — and whether those two jobs reward different model sizes.

This explores not how to predict AGI's arrival, but how the act of forecasting itself should change once AI systems become forecasters, actors, and economic agents — the corpus reframes the question from "predict the date" to "redesign the method."

This explores why some errors, costs, or interferences get harmlessly absorbed while others compound or sit at chokepoints that derail an entire process — and the corpus answers it less as a question about size than about position and propagation.

This explores why smaller or weaker models can't reliably reach for and use the scaffolding — memory, skills, tools, protocols — that a harness provides, even when that scaffolding is sitting right there for them to use.

This explores why models that get better at reasoning often get worse at doing exactly what you told them — and whether that's a genuine trade-off or a fixable training artifact.

This reads the question as a head-to-head — do agents that coordinate as peers handle growth better than a top-down command structure — and the corpus suggests the real answer is that neither pure form scales, while a hybrid that fixes structure but frees roles wins.

This asks whether keeping humans in the loop actually makes AI's self-improvement loops both faster and safer — or whether those two goals trade off against each other.

This explores whether a single end-to-end streaming model that jointly handles listening, thinking, and speaking can manage conversational turn-taking better than the traditional three-box pipeline of speech recognition, then language model, then speech synthesis.

This explores what gets lost when speech is converted to text first — the acoustic, articulatory, and prosodic information that direct speech-to-speech systems keep but a transcript throws away.

This explores why preserving the full key-value detail of context (rather than squeezing it into a smaller summary or fixed-size state) protects exactly the capabilities — copying, retrieval, fine distinctions — that compression quietly destroys.

This explores how self-supervised speech models pick up the bodily mechanics of how a vocal tract makes sound — without ever being told which sound is which phoneme.

This explores whether generating speech directly from speech input actually removes the delay that comes from transcribing audio to text first — and what the corpus says is really being saved (and what's being traded away).

This explores whether sparsity has to be designed into the training objective itself — not bolted onto a finished model as a hardware shortcut — for the GPU savings to come without a quality penalty.

This explores whether sparse attention bolted onto an already-trained dense model can rival sparse attention that was learned from scratch during pretraining — and what the corpus says about why that gap exists.

This explores why speech evaluation keeps scoring how accurately a model writes down words (transcription) rather than whether it grasps meaning — and what that choice does to the models we build.

This explores two ways to get more 'thinking depth' out of a small network without adding parameters — stacking two recurrent timescales (hierarchical recurrence) versus re-running a chosen subset of layers in a loop (selective layer looping) — and what the corpus says about how they differ.

This explores whether applying a fixed sparsity pattern to every token — sparse attention that doesn't adapt to how long the input actually is — recreates the known error of fixed sparse-attention budgets, specifically in the short-sequence regime where there's less redundancy to throw away.

This explores whether looping computation — re-applying the same layers over and over at inference time instead of building bigger models — actually buys you efficiency, and whether making that looping *flexible* (more loops for hard problems, fewer for easy ones) pushes the gains further.

This explores whether spending more compute on a ranker (deeper cross-encoders, reasoning chains, LLM judges) actually buys resilience to adversarial content — and the corpus suggests the premise is shakier than it sounds.

This explores whether models that reuse their own layers in a loop — recursing on a reasoning state instead of stacking more fixed layers — can solve problems that ordinary fixed-depth networks provably cannot.

This explores whether tracking where content came from — tagging it as human-written, AI-generated, or verified — can actually keep machine-made text from contaminating a knowledge base, and the corpus suggests provenance is necessary but does most of its work as a *gate at write-time*, not a label after the fact.

This explores why looping the same layers or computation blocks back over the model's own working state (rather than adding more parameters) tends to make models better at hard reasoning — and where that gain comes from.

This explores whether sameness is itself a vulnerability — how systems built from identical, agreeing parts collapse under attack or pressure in ways that systems with built-in diversity and disagreement don't.

This explores whether the scaffolding around an agent — its memory, skills, and the wiring that connects its steps — can be learned and improved the way a model's weights are, rather than staying a fixed hand-built harness.

This explores whether the way rare, low-frequency items get squeezed out during training (the 'tail' collapsing) is the same force that explains where retrieval systems fail — and the corpus suggests these are two related-but-distinct failure stories that rhyme more than they overlap.

This explores which jobs we currently ask the model's weights to do — planning, memory, skill, calibration of effort — that research suggests belong instead in the scaffolding around the model (the 'harness'): the memory stores, tool protocols, and orchestration logic.

This explores why long-context models burn compute reshaping incoming text into the model's working representation (its internal state / weights / cache) instead of just parking the raw text in memory and reading it back later.

This explores why agents stay reliable across long workflows when their working state lives in a bounded, schema-governed structure rather than being reconstructed by replaying the whole conversation transcript.

This explores how 'Soft Thinking' lets a model keep many reasoning routes alive at once by reasoning in continuous concept space — instead of picking one discrete word per step, which forces a single path.

This explores why reasoning models break down — and the corpus's answer is that failure tracks how unfamiliar a specific problem instance is, not how many steps the reasoning chain requires.

This explores whether models can do their 'thinking' inside hidden internal states — scaling up reasoning at inference time — without writing out chain-of-thought tokens and without a special training regime to teach them how.

This explores whether *where* the work concentrates in a reasoning trace — a small set of pivotal tokens or steps — predicts answer quality better than the trace's raw length, and the corpus says the concentration matters and the length mostly doesn't.

This explores whether the techniques that make reasoning cheaper and shorter still hold up on open-ended tasks — the kind without a clean, verifiable chain of correct steps to lean on.

This explores a real complexity-theory boundary: which kinds of problems can't be cracked by throwing more parallel compute (wider sampling, more votes, bigger Transformers) at them, and instead need genuine serial depth — step building on step.

This explores why throwing more tokens at a hard problem doesn't rescue models that 'collapse' on it — and the corpus's surprising answer is that the budget was never the binding constraint.

This explores why training a model to reason harder — longer chains of thought, more RL and SFT for problem-solving — tends to make it worse at obeying the explicit instructions you gave it.

This explores whether training models to reason — chain-of-thought, RL on reasoning traces — makes them worse at noticing when a question's starting assumptions are wrong, charging ahead on a familiar template instead of stopping to question the premise.

This explores how much of a chain-of-thought (CoT) trace does real computational work versus just filling space — and what the corpus says about cutting the slack.

This explores whether bolting chain-of-thought reasoning onto a model can actively make it worse at certain tasks — specifically inductive rule inference, where you learn a rule from examples including exceptions.

This explores whether adding chain-of-thought reasoning before a reward model scores an answer actually produces better judgments — or whether the reasoning is decorative.

This explores whether grading reasoning by its final answer — an outcome-focused objective — is itself the reason we keep misdiagnosing where and why reasoning models fail.

This explores why language models tend to lock onto an answer and then justify it — accommodating false premises, defaulting to safe choices, or hiding the real reasoning behind a confident-looking output — rather than where that bias literally lives in the network.

This explores why chains of reasoning that are logically broken or even nonsensical still produce correct answers — and what that tells us about whether the reasoning is doing the work, or just the look of reasoning is.

This explores whether you can merge fine-tuned weights or adapters (like LoRA) into one model when the pieces don't line up cleanly — and what the corpus says about why naive composition breaks and how to rescue it.

This reads the question as: if a network generates lightweight model adapters on the fly, can we inspect those adapters to confirm they behave correctly and don't smuggle in bias — and the corpus answers obliquely, through adapters-as-state, backdoored checkpoints, and the machinery of verification.

This explores whether you can take several LoRA adapters — each fine-tuned on an already-aligned base — and combine them by literally adding or merging their weight deltas, rather than retraining a single multi-skill model.

This explores whether compiling agent skills into model weights (LoRA adapters, hidden-state interventions) throws away the richness of the same skill written out as plain-text instructions — and what each form is actually good at.

This explores whether a skill document can be both auto-optimized like model weights (text-space optimization) and kept under a human-auditable approval gate (governance) — and whether those two goals fight each other or reinforce each other within one workflow.

This explores why dropping expertise into a prompt and hoping it transfers can't stand in for knowledge that's versioned, inspectable, and correctable — and what the corpus says breaks when you try.

This explores how a deployed skill keeps two things apart and independently inspectable — what an agent knows (capability) versus how it actually acts (behavior) — so each can be audited, corrected, or rolled back without contaminating the other.

This explores whether being able to read a person-grounded skill file actually proves the resulting behavior faithfully reflects the person it's distilled from — or whether inspectability and fidelity are two different guarantees.

This explores whether a reliable scoring or verification signal is enough to keep iterative loops — self-improvement, refinement, learning-from-your-own-output — from quietly accumulating errors as they run.

This explores whether what scales an agent is the quality of useful feedback it actually absorbs — not the sheer count of tokens or tool calls it burns through.

This explores why frontier models give up or stop short on tasks that require sustained, multi-step effort over long horizons — and what the corpus says actually separates the models that keep going from the ones that quit.

This explores why long, multi-step tasks get worse over time as the model drags its full history along — and what the corpus says about treating accumulated context as a liability rather than an asset.

This explores why models that compute a single deterministic next-state — one fixed latent update per step — structurally can't hold several candidate solutions at once, and what the corpus says about the architectural fix.

This explores whether re-applying a model's layers over its own hidden state ('latent recurrence') can reach reasoning depths that fixed-depth transformers are mathematically barred from — and where that trick stops paying off.

This explores how methods like Soft Thinking keep a model reasoning across several possible paths at once — instead of committing to one word at a time — and what that reveals about where reasoning actually lives.

This explores why randomness that's tied to a principled training objective or aimed at the right decision points helps reasoning, while undirected noise sprinkled into a model does nothing.

This explores whether the connections inside an agent's memory (which items link to which) should be rewired on the fly using signals from how tasks actually turn out — and what the corpus says about doing that well.

This explores the tradeoff between *rebuilding* what you need on the fly — traversing a memory graph, consolidating context into internal state, reasoning your way back to relevant facts — versus just *handing the model the raw text* and letting it read. The corpus suggests reconstruction wins on hard reasoning and loses on plain retrieval.

This explores whether agents break down over long multi-turn workflows because they lack the right knowledge, or because they can't govern what enters and stays in their working memory — and the corpus comes down firmly on the memory-control side.

This explores whether the idea of 'iterative depth' — letting a model loop and refine its internal state over and over instead of just getting bigger — actually helps when the thing being modeled is a physical world that unfolds step by step.

This explores why writing reasoning out as tokens (chain-of-thought) compensates for something the transformer's fixed-depth, feedforward architecture can't do on its own.

This explores why agent memory works better when its stored pieces are well-linked and reachable than when there's simply more of it sitting in storage.

This explores whether recursion — re-running a small network on its own evolving reasoning state — can beat raw parameter count on hard reasoning puzzles, and what the corpus says actually drives that advantage.

This explores why a model can better decide *when to stop thinking* by watching its own internal state settle into a stable point (fixed-point convergence) than by training it to emit a special 'I'm done' token (a learned halt token).

This explores a real tension: almost every tool we have for grading reasoning—process rewards, reflection-token analysis, trace correctness—operates on visible text, so what happens when the reasoning lives in hidden state and never surfaces as words?

This explores the difference between widening a reasoning system by running many loops side-by-side (parallel trajectories) versus deepening it by stacking loops one after another (sequential recurrence) — and why the choice isn't free.

This explores whether looped models — which re-apply the same layers over and over to refine an answer — can be engineered so the later passes settle down instead of wobbling between states, and what the corpus says actually causes that wobble.

This explores whether the number of times a looped/recurrent model re-runs its computation should be baked in during training or chosen dynamically per-input at inference — and what the corpus says about who decides when to stop.

This explores whether looping a network's layers — re-running the same block over and over — can do more than repeat the same step, actually building up new kinds of computation a fixed-depth network can't reach.

This explores why, in looped/iterative language models, the *second* pass through the computation does the heavy lifting of refinement — while the first sets up and later passes add little or even hurt.

This explores whether 'looped' or recurrent transformer blocks — ones that feed their own latent state back through the same layers repeatedly — actually settle into a stable resting state, and whether that settling is reliable enough to build on.

This explores what actually happens inside a language model as it produces an answer — the internal phases of computation across layers, not the visible chain-of-thought it prints.

This explores whether a model has to spell out its reasoning in words to reason well — or whether the visible 'thinking' is partly performance the corpus suggests can be compressed, hidden, or skipped.

This explores why looping or re-applying the same layers (reused computation) often beats simply stacking more layers to make a model deeper — and what recursion buys that raw depth doesn't.

This explores whether re-applying a model's computation in a loop — recursion — can produce better generalization than simply making the model bigger, and what the corpus says about where each strategy's power actually comes from.

This explores why language is full of repetition, grammatical scaffolding, and 'filler' that humans seem to rely on, yet models can strip away with little loss — and what that asymmetry reveals about how each side actually uses words.

This explores whether models that 'think' in their own internal representation space — rather than predicting one token at a time — actually learn faster and run leaner, and where that advantage holds or breaks.

This explores the tension between models communicating in compact latent representations (faster, denser than passing text back and forth) and our ability to read what they're actually saying — and whether the corpus offers ways to keep both.

This explores what oversight breaks down when agents stop talking in plain language — sharing latent vectors, KV caches, or hidden states instead of readable text — and what new failure surfaces that opens up.

This explores why reasoning expressed in words doesn't suffer the 'drift' that plagues latent (vector-space) reasoning — and the corpus suggests the answer is that text is its own anchor, though that anchoring comes with a hidden cost.

This explores how the way LLMs squeeze concepts down (for efficiency) differs from how humans organize concepts (for usable, situated meaning) — and what the corpus reveals about the trade-offs each strategy makes.

This explores why, in models that loop the same layers over and over, the predictions made at intermediate steps tend to agree with the final answer — and what that says about what looping actually computes.

This explores how much human-readable verbal content a model's reasoning steps actually need — whether latent or compressed reasoning can drop most of the words and still think, or whether the explicit verbal signal is doing the real work.

This explores whether learning to *regenerate* data from a latent space (JEPA-style next-embedding prediction, generative judging) keeps the shape of the underlying data manifold more faithfully than squeezing it into a fixed-dimension geometric code (embeddings, vector compression) — and the corpus suggests the geometric route hits hard mathematical walls that generation sidesteps.

This explores whether step-by-step (process) feedback restores reasoning accuracy more effectively than answer-only (outcome) rewards — and reads the 'latent space' angle as a question about where reasoning actually lives in the model.

This reads 'discourse structure' broadly — not just sentence grammar but how meaning gets built across a conversation: topic, grounding, presupposition, and stance — and asks where LLMs systematically come up short.

This explores the tension between models scoring their own work (self-improvement) and the external reward signals that training usually relies on — and the corpus's clear verdict is that the two need each other.

This explores why so many ways AI reasoning breaks down trace back to the same root: the model never built a real internal picture of how things work, only a pattern-map of how answers tend to look.

This explores whether adding more agents actually buys you more reasoning power than a single model — and the corpus answer is: less than you'd think, because most of the apparent gain is just spending more compute.

This explores the recurring throughlines that connect two bodies of work — methods for spending more compute at inference time (test-time scaling) and the designs that make models reason — and what those bodies of work, read side by side, reveal about each other.

This explores how the way you train a model — the objective you optimize for — quietly reshapes what it can be trusted to do, often degrading reliability in ways the training signal never penalized.

This explores why standard transformer attention—a soft, pairwise weighting over tokens—struggles to represent the higher-order, relational structure that graphs encode, and what the corpus offers as workarounds.

This explores how two models can score the same on benchmarks while organizing knowledge completely differently inside — and why that internal divergence matters even when the scoreboard doesn't show it.

This explores whether generating text by tweaking a whole sequence at once with gradients (as diffusion models do) can hit targets that strict left-to-right, one-token-at-a-time generation structurally can't.

This explores whether redesigning the model itself — its depth, attention ratios, internal structure — can deliver the win of spending the right amount of compute on each prompt, or whether that goal depends on things architecture can't touch.

This explores why a reasoning model's *confidence* — settling smoothly on an answer and not wavering — can be a false signal of correctness, and what better signals the corpus offers.

This explores the claim that fixed-point detection makes a reliable 'stop computing now' signal in looped/recurrent transformers — the corpus speaks directly to the fixed-point halting idea, though it doesn't isolate pre-norm normalization as the mechanism behind it.

This explores why diffusion language models can generate text faster than autoregressive (AR) models that emit one token at a time — and the catch that the speedup isn't free.

This explores a recurring gap: a system can be provably stable or self-consistent in its internal (latent) behavior and still fail to be faithful — accurate, truthful, or true to reality.

This explores whether looped transformers — models that re-run the same layers over and over instead of stacking more — actually settle into a stable 'fixed point' as they iterate, and whether that settling is useful.

This explores the claim that a 'world model' isn't one problem but five separate design decisions that have to fit together — and what goes wrong when you treat them as a single thing.

This explores why long-horizon world-model rollouts tend to drift or blow up, and how a mathematical cap on how much each prediction step can amplify the state (spectral-norm constraints) keeps that error from compounding.

This explores what you gain by making continuous latent reasoning probabilistically tractable with normalizing flows, versus letting a model 'think' in raw vector space where you can't sample, score, or train on those thoughts.

This explores whether the trick that makes reasoning work — re-running computation in a loop to deepen a model's thinking rather than adding parameters — also works when a model has to predict how an environment will change, i.e. world modeling.

This explores whether models can stretch their 'thinking budget' at inference time by iterating on hidden internal states — reasoning in latent space — instead of generating visible word-by-word chains of thought.

This explores whether you can make a language model more capable by scaling something other than its parameter count — specifically latent vectors (compact internal representations the model learns to reason over) — and how those two levers play off each other.

This explores whether reasoning has to be spelled out in words (chain-of-thought) for us to train it and steer it — or whether reasoning can live in a model's hidden states and still be shaped and controlled.

This explores whether high scores on checkable tasks (math, code, puzzles with right answers) actually predict performance on problems the model never trained on — or whether they're inflated by memorization and break down off-distribution.

This explores why reasoning with checkable answers (math, code) scales cheaply and reliably, while open-ended reasoning — judgment calls with no clean right answer — resists the same scaling tricks, and what the corpus says actually causes the gap.

This explores whether curriculum training — feeding small models problems matched to their current ability and advancing as they improve — actually keeps them learning, or whether it stalls; the corpus suggests the 'learning edge' is a real, measurable frontier, and staying on it is the whole game.

This reads 'escalate vs. aggregate' as a design choice: when should a multi-agent system hand a decision upward — to a human, to a held-open set of competing answers, or to a different process — instead of forcing its members to vote, average, or merge into one verdict?

This explores why averaging toward agreement is harmless when there's a right answer (factual tasks) but lossy when the spread of positions is itself the signal (normative tasks) — and what the corpus says about consensus that erases rather than resolves.

This explores why reinforcement learning only delivers reasoning gains when the base model still has untapped capacity ("headroom") and is trained on problems sitting right at the boundary of what it can already do — and the corpus has a clear mechanistic answer.

This explores whether warming up a model by imitating good examples first, then letting it explore via reinforcement learning, beats doing either step on its own — and why the order matters.

This explores whether you can read a model's reasoning trace and tell the difference between two outputs disagreeing because they hit a real values trade-off versus disagreeing because one of them simply reasoned wrong.

This explores why AI conversations get worse the longer they run — and the corpus points to one dominant cause (the model misreading what you want early) plus a few quieter ones (context crowding, persona drift).

This explores whether a single end-to-end model can stay inspectable and fixable when something goes wrong — or whether you need separate, observable parts (planners, verifiers, tool calls) to know where a failure happened.

This explores why pipeline-style dialogue systems — where one module's output (speech recognition, intent parsing, dialogue management, generation) feeds the next — let small errors compound into large failures at the handoffs between stages.

This explores what you actually gain in speed when you stop chaining together separate speech-to-text, language, and text-to-speech stages and instead let one model handle voice end to end.

This explores the difference between two kinds of disagreement an agent's reasoning leaves behind — disputes about what the input *means* (interpretive) versus disputes about what the right *call* is once the facts are settled (evaluative) — and how each leaves a different fingerprint in the trace.

This explores a tradeoff baked into attention: it's superb at reaching back and pulling a fact out of context, but doesn't easily fold that context into a compact, updatable internal state — and the corpus suggests the two abilities pull in opposite directions.

This explores the architectural showdown between transformers and state-space models (SSMs like Mamba) specifically on copying long strings and pulling facts back out of context — and why the gap exists.

This explores what specific capabilities you get from re-applying the same layers in a loop (recurrent depth) — things that simply making a wider or even deeper fixed-stack feedforward network can't deliver.

This explores whether the *shape* of a reasoning trace — its planning, backtracking, step-by-step layout — is a learned formatting habit the model picks up from training, rather than a record of actual computation.

This explores the gap between the story a reasoning trace tells in language (discourse structure) and the actual computation that produced the answer (mechanistic causal structure) — and why the two don't line up.

This explores whether process reward models (the systems that score a reasoning chain step-by-step) can cope with messy thinking traces that branch, backtrack, and abandon dead ends — rather than clean, linear final answers.

This explores whether looping a transformer's computation back on itself (recurrence) lets it track evolving state more cheaply than a standard feedforward stack — and what the corpus says about why that helps.

This explores how much of an LLM's chain-of-thought is functional — actually driving the answer — versus decorative steps the final answer doesn't depend on.

This explores why model reasoning traces *look* like human step-by-step thinking — and what the corpus says is actually happening underneath that human-style narrative.

This explores how a model's baked-in training knowledge (its 'parametric' memory) can override the information you actually hand it in the prompt — so the model answers from what it already 'knows' instead of from the context in front of it.

This explores whether knowing-when-to-say-'I-don't-know' (abstention) is a capability that scales up from small models to frontier ones — and the corpus reframes it as a trainable behavior that's undertrained at every scale, not a property you inherit by getting bigger.

This explores what actually drives a model to abstain — to say 'I can't answer that' when it lacks grounding evidence — and why that behavior is so fragile in practice.

This explores why training a model to hand off subtasks to other agents makes it better even when it later works solo — what the delegation skill actually teaches.

This explores whether citations actually do their job—grounding answers in evidence—or whether they decouple from the reasoning and become decorative trust signals that humans and models alike respond to without checking.

This explores why citations function as a trust signal that's largely detached from whether the cited sources actually support the claim — and what that decoupling reveals about how people read AI answers.

This explores two separate questions hiding inside delegation — the *when* (at which moments handing off is worth the overhead) and the *what* (which subtasks are actually good candidates) — and what the corpus says about whether models are any good at making those calls.

This explores the twin control problems for agent memory — the *write gate* (what's allowed to become a permanent memory) and the *trust gate* (how stored memories earn the right to be believed later) — and what the corpus suggests about designing both.

This explores whether the act of delegating work to LLM agents can itself stop the slow, invisible accumulation of errors that creeps into long multi-step workflows — and the corpus suggests delegation is usually the cause of that corruption, not the cure.

This explores when knowledge a model carries forward from one continual-learning task bleeds into another as false generalization — and what structural factors decide whether that carryover helps or corrupts.

This explores whether in-context learning — adapting from examples in the prompt without weight updates — loses its edge once a running interaction grows longer than what the context window can hold, and what the corpus offers as the fallback.

This explores why an agent that compresses and merges its accumulated memory can end up worse than one with no long-term memory at all — the failure isn't too little memory, it's the act of consolidation itself.

This explores why feedback grounded in something checkable — code that runs, a formal verifier, a structured proof obligation — tends to catch errors that one model simply reading another model's prose answer in natural language will miss.

This explores 'program-aided' or code-based reasoning — the idea that instead of reasoning in prose, an LLM offloads the actual computation into code or tool calls that something else runs, and why that shift matters.

This explores the limits of the 'code-as-reasoning-substrate' idea — when expressing an agent's thinking as executable code stops helping and starts hiding the work the model actually has to do.

This explores the design moves — not bigger models — that close the gap where AI produces plausible work faster than anything can check it, so verification stops being the bottleneck.

This explores how an agent's environment state shows up in two concrete records — the step-by-step execution trace of what it did, and the tests or checks run against it — and what those records actually capture.

This explores the gap between provenance (knowing where a claim came from) and soundness (whether the source is actually any good) — the corpus suggests these are two different questions that get quietly collapsed into one.

This explores how splitting writing work across specialized agent roles—rather than asking one model to draft everything—holds a long document together, and why that division of labor fights the consistency problems that sink single-model long-form generation.

This explores why citation count works as a trust signal that's decoupled from whether the citations actually support the claim — and what the corpus says about the deeper mechanism: trust attaches to the *signs* of authority rather than the substance.

This explores what happens to the unusual, hard-to-verify, minority lines of reasoning — the 'long tail' — when AI is brought in to help groups think and decide together, and the corpus suggests the tail gets squeezed from several directions at once.

This reads the question as: if you build a system that tags retrieved memories by their *function* (clarifying, irrelevant, etc.) before using them, how good does that tagging classifier have to be before it helps rather than hurts — and the corpus reframes the question more than it answers it numerically.

This explores whether prompting tricks meant to make a model produce a wider range of viewpoints actually let it cover the real spread of human arguments — or whether they just rearrange what the model already leans toward.

This explores whether personalization should store distilled, abstract preference summaries instead of replaying a user's specific past interactions — and the corpus has a sharper answer than you'd expect.

This explores why dumping everything an agent has seen — full transcripts, unfiltered retrieval, auto-consolidated history — back into its context causes long workflows to break down, and what controlled alternatives the corpus offers.

This explores whether LLMs land on the same argumentative shapes because they reason their way there independently, or because they're all compressing the same underlying structure of language — and the corpus leans hard toward the second.

This explores what property a sub-goal must have to generate frequent, fine-grained training signal rather than a single pass/fail at the end — and the corpus suggests verifiability is less about a goal being 'objectively checkable' and more about whether it can be decomposed into many small criteria or instrumented for an intrinsic progress signal.

This explores why a *spread* of viewpoints beats any single well-built argument — and what the corpus says happens when AI floods us with claims that all come from roughly one perspective.

This explores why a back-and-forth chat system has a harder time with vague queries than a fixed search index does — and what the corpus says the missing ingredient is.

This explores why evolutionary/population-based methods (keeping a diverse pool of candidates that mutate and recombine) beat the two simpler test-time strategies — sampling many answers at once (parallel) and refining one answer step by step (sequential).

This explores whether you can stitch together fragments of separate reasoning chains and still get something coherent — and what the corpus reveals about why natural-language reasoning is more (or less) recombinable than it looks.

This explores the failure modes that emerge specifically from shared, mutable artifacts in multi-agent systems — what goes wrong when several agents read, write, and revise the same files, documents, or state rather than working in isolation.

This explores how you check whether agent-written or agent-run code is correct when you can't fully execute it or when the usual success signals (final-answer pass/fail) are missing or unreliable.

This explores how agents decide which code they write during a task is just scratch work versus what deserves to be saved, shared, and promoted into durable infrastructure — and the corpus suggests this 'lifecycle' decision is one of the least-settled problems in agent design.

This explores the limits of backward decomposition — breaking a goal into sub-steps from the target back to the start — and asks where that strategy stops working: open-ended tasks with no clean target state, or unstructured ones where the steps can't be cleanly separated.

This explores whether agents that write their own reusable skills can keep getting more capable through accumulation — and what the corpus says limits or accelerates that compounding.

This explores how the checking machinery around a model during training — reward functions, verifiers, trajectory filters — ends up authoring the model's habits, because a model learns to satisfy whatever scores it, not the thing you hoped it would learn.

This explores whether one benchmark number can capture agent ability when 'capability' is actually layered across distinct dimensions — and the corpus says no, that's the core failure of how agents get measured today.

This explores why the third layer of agent code — the artifacts agents write for themselves during execution — gets the least attention, even though it's where new capability could compound.

This explores the path by which code an agent writes for itself during a task gets promoted into the durable scaffolding (the harness) that future runs and other agents rely on.

This explores whether a stronger shared base model automatically makes lightweight personalization adapters (PEFT/LoRA-style deltas) more useful for a diverse population of users — or whether usefulness hinges on other factors.

This explores why the two most common personalization shortcuts — stuffing context into prompts and retrieving a user's past interactions — fall short of capturing who a user actually is, and what the corpus suggests works better.

This explores whether you can give every user their own lightweight fine-tuned adapter on a shared base model and keep each one stable over time (no drift) without their learned traits bleeding into other tasks or users (no leaking).

This explores why looping a small network back over its own intermediate state ('recursive depth') beats simply stacking more layers or piling on parameters ('parametric depth') on structured puzzles like ARC-AGI.

This explores the mechanical difference between two ways a model can 'think': recursing on an internal latent state (reasoning that never becomes words) versus chain-of-thought, where the model writes out intermediate steps as text and reads them back.

This explores whether the two-timescale 'hierarchy' in models like the Hierarchical Reasoning Model is actually doing the work — or whether the recursion alone is what improves reasoning, so hierarchy could be dropped without loss.

This explores why fine-tuning a model on a single user's data to personalize it tends to wreck its broader reasoning ability — and what the corpus says is actually breaking.

This explores how you can stack many task- or user-specific adapters on one base model without their learned changes colliding — and the corpus reframes that less as a geometry trick ('make the vectors orthogonal') and more as a question of isolating which parameters each adapter is allowed to touch.

This reads 'ghost-authored records' as AI-fabricated or machine-generated academic papers, and asks why the publishing/indexing pipeline can't just auto-reject them — what makes the fakes pass the filters.

This explores why letting a small network loop over its own internal reasoning state — re-running the same layers — produces better generalization than simply adding more parameters, and what the corpus says about depth/recursion as a scaling axis distinct from size.

This explores not just the raw share of the web that's AI-generated, but the deeper problem the corpus keeps circling: that disclosure barely matters because we've lost the ability to tell the difference at all.

This explores how agents can improve their own capabilities — generating their own training signal, feedback, and curriculum — when no human is in the loop to label, demonstrate, or reward.

This explores why models with different sizes, architectures, and training regimes tend to produce similar answers on open-ended tasks — and the corpus suggests the convergence is built into the shared autoregressive objective, not a coincidence of training data.

This explores whether the best way to catch AI-generated text is a signal baked in at generation time (token watermarks) versus reading the writing style after the fact (stylometry) — and the corpus actually pushes back on both framings, pointing toward a third signal: structure.

This explores a counterintuitive finding: agents lean on raw interaction logs and largely disregard the tidy summaries built from them — and asks why that happens and what it implies for how we build agent memory.

This explores what distinguishes a skill worth saving to a persistent, external scaffold (memory, skill libraries, reusable code) from one that should stay inside the model — and why externalizing pays off.

This explores whether the value you get from a harness — the memory, skills, and protocols wrapped around a model — changes depending on whether you're running a weak, mid-tier, or frontier model.

This explores whether the procedural know-how an agent uses — its 'skills' — has to live in the prompt as text, or whether it can be baked into the model's weights (or stored elsewhere entirely), and what each choice costs.

This explores why the field has poured effort into teaching agents what action to take next (the policy) while largely neglecting the agent's internal model of how its environment will respond (the world model) — and what that imbalance costs.

This explores whether a world model is one thing or two — a store of facts about the world versus a generative engine that can run forward, simulate interventions, and answer 'what if' — and what the corpus says about how (and whether) those two parts come apart.

This explores whether the realism of a learned simulation (a 'world model' an agent trains inside) caps what the agent can actually learn — i.e., does the agent only ever learn what the simulator was good enough to show it?

This explores whether training a model to predict the next state of the world actually teaches it how the world works — a genuine causal model you can reason with — or whether it just gets very good at pattern-matching observed sequences without understanding the underlying structure.

This explores whether scoring how an agent gets to an answer — its trajectory, not just its final output — actually tells you anything about whether the agent is safe to deploy and worthy of a user's trust.

This explores what changes when you keep the model fixed but wrap it in different scaffolding — the harness of prompts, tool loops, memory, verifiers, and access level through which a model's behavior is actually expressed.

This explores whether keeping an agent's memory clean and reliable (no error buildup, no stale context) is actually at odds with keeping its token usage lean — or whether the two goals can be served by the same design.

This explores whether letting agents find each other and find capabilities dynamically — rather than wiring connections by hand or routing through a central registry — makes agent protocols easier to design and more likely to get adopted.

This explores what a single, shared standard for agents talking to each other and to their tools would have to look like in practice — and the corpus's answer is less 'one grand protocol' and more 'a thin bridging layer over messy reality.'

This explores whether embedding an agent's abilities as searchable, version-stamped vectors can fix the hardest part of agent protocols like MCP — letting one agent find the right collaborator or tool without someone hand-wiring the connections in advance.

This explores whether the work of checking that an output is correct can be tracked as its own quantity — separate from how fast the system produces that output — and what the corpus says about why you'd want to.

This explores why the same trait that makes MCP easy to plug into many systems — its rigid, portable schema — is also what produces flaky, non-repeatable behavior once agents run for real.

This explores a sequencing argument in preference-based AI training: averaging or pooling preference data can't rescue measurements that were flawed to begin with, so the question is what 'valid' even means before you start combining signals.

This reads the question as: what does the formal theory of fairly aggregating individual preferences into a collective choice — voting rules, equal weighting, welfare aggregation — actually buy us when we try to align AI with human values, and where the corpus says it breaks down.

This explores whether reward models that hide a latent variable inside them can represent preferences that split into several distinct peaks — different user groups, or even multiple tastes inside one person — rather than collapsing everyone into a single 'average' preference.

This explores why training a single reward model on pooled human preferences doesn't just average out minority views — it structurally erases them, and what the corpus offers as alternatives.

This explores whether LLMs are genuinely better at reasoning about cause-and-effect than at reasoning about what happened in what order — and why that gap exists.

This explores what would help LLMs tell apart 'A caused B' from 'A then B' — and the corpus points less toward retraining the model and more toward bolting a separate causal structure onto it.

This explores how *choosing which frames to look at* and *ordering retrieved evidence by time* — rather than sampling video at a fixed interval — helps models reason about what happens across a long video.

This explores why an LLM's left-to-right token generation produces a sequence — one token after another — without the lived, reflective duration we mean by 'time,' and what that gap costs.

This explores a design tradeoff: when you want a model to know *you* — your history, preferences, situation — is that knowledge better delivered as text in the prompt (tokens) or baked into compressed vectors the model reads internally (learned representations)?

This reads the question as: when one unified model is asked to do everything, some capabilities come out strong and others stay weak — can changing the architecture (not just adding scale or data) close that gap, or is the unevenness baked into the design? (Note: the corpus here speaks to uneven *computational* capability across tasks, not fairness-style demographic representation — if you meant the latter, this collection doesn't cover it directly.)

This explores whether learning a small set of reward-function parameters (compact numeric representations of what a user wants) can outperform describing a user in natural-language text — and the corpus suggests the honest answer is 'it depends what you're optimizing for,' because the two approaches win on different axes.

This explores whether personalization should track how a user's tastes shift over time rather than relying on a fixed profile — and the corpus suggests the real answer reframes the question: drift and stability aren't rivals, they're two signals that have to be modeled separately and at the right grain.

This explores how a model's tilt toward Western, high-resource cultures gets baked in — not at the visible output layer, but through what its training images and text most frequently show, and how that frequency hardens into the model's internal representations.

This reads the question two ways at once: do models systematically mishandle culturally rare concepts, and does scaling them up make that failure *predictable* rather than random — and the corpus suggests the failure is structural and predictable, but not in the direction 'more scale = more failure.'

This explores whether external tools (code execution, structured cognitive operations) genuinely extend a model's *reasoning* into territory that demands insight — not just whether they speed up the arithmetic.

This explores why shaping the *advantage* (the post-baseline signal that tells a model how much better an action was than its peers) tends to train tool-use more stably than shaping the *reward* itself (injecting bonuses and penalties into the raw score before the math), and what the corpus says about where reward shaping goes wrong.

This explores how the way we test reasoning — text-only vs. tool-enabled, short vs. padded inputs, familiar vs. novel instances — can change what looks like a model's 'reasoning ability,' often more than the model itself does.

This explores why letting a model call external tools breaks the old assumption that knowing more facts requires a bigger model — and what the corpus says about where capability actually lives.

This explores why Branch-Train-Merge — training expert models separately and stitching them together — depends on a *learned router* to pick which expert handles each token, and what breaks when you skip that routing step.

This explores whether you can train and combine specialized LLM 'experts' that never had to talk to each other during training — and whether the same idea of synchronization-free composition extends from training to inference and multi-agent coordination.

This explores what actually lets a Mixture-of-Experts model route each token to the right specialist — and the corpus turns out to answer it sideways, since it has little on classic gating internals but a lot on how experts get built, merged, and selected.

This explores two different ways to carve up 'expertise' inside an LLM — by subject area (law, medicine, code) versus by time period (recent vs. historical) — and what the corpus knows about each as an engineering and a failure-mode problem.

This explores whether smaller models can generate updates to skills, harnesses, or instruction-libraries that are as useful as those written by frontier models — and the corpus suggests the surprising answer is mostly yes for *producing* the update, with the real bottleneck showing up elsewhere.

This explores why moving an agent's working memory, procedures, and rules out of the model and into a surrounding 'harness' layer makes the agent more dependable than just using a bigger model.

This explores why a model that already holds a relevant capability — a reasoning step, a stored fact, a usable skill — fails to fire it at the moment it's needed, rather than why it lacks the capability at all.

This explores a design fork in self-improving agents: do you put the learning into the executor that does the task, or into the separate 'evolver' that rewrites the agent's skills, prompts, and harness — and the corpus increasingly points toward training the evolver while freezing the executor.

This explores a counterintuitive finding: training models to reason harder (the thing that makes them "strong") often makes them worse at actually following the instructions you gave them.

This explores why moving an agent's memory, skills, and reasoning into external scaffolding (a 'harness') tends to produce more reliable behavior than simply using a bigger or better-trained model.

This explores why storing facts inside a model's weights is a worse deal than letting it look things up with a tool — and what the corpus says about where in-weight memory hits its limits.

This explores whether writing new facts directly into a model's weights through fine-tuning damages or erases what the model already knew — and what the corpus suggests about where that damage lives and how to avoid it.

This explores how giving language models external tools (code execution, calculators, function calls) changes what they can actually accomplish — and whether the gains are real expansion or just convenience.

This reads as asking what you actually *get* — in speed, memory, and context length — when you swap quadratic attention for the linear, fixed-state machinery of state-space models, and what that efficiency costs you.

This explores whether a compressed, fixed-width memory (the kind state-space models and recurrent architectures carry forward) can hold everything in a long input without losing information — and what the corpus says about the limits of that ambition.

This explores how recurrent memory architectures (which compress the past into a carried-forward state) cope with million-token contexts in a fundamentally different way than attention (which re-reads every token), and what each approach trades away.

This reads the question as: what does softmax/full attention actually contribute that pure linear attention throws away — such that bolting a little of it back on (the 'hybrid') beats going fully linear?

This explores trust *calibration* — whether we can tell when a user's trust in an AI assistant actually matches the system's reliability, rather than just measuring how much they trust it.

This reads the question as: what keeps a growing population of AI agents from coordinating in ways that cause harm — and the corpus reframes the worry, because it suggests the bigger risk is coordination that fails badly, not coordination that turns malicious.

This explores why people credit assistant-style LLMs with genuine social skill — tact, warmth, conversational judgment — when those behaviors are actually trained surface patterns, and what in the corpus explains the gap between how socially competent these models seem and what they're actually doing.

This explores whether AI assistants should be tuned to the standards of the social role they're playing (a doctor's assistant, a teacher's aide) instead of just maximizing what an individual user says they want — and what goes wrong with the preference-maximizing default.

This explores whether changing attention's computational complexity (linear/sparse attention) buys you the same kind of efficiency as shrinking the weights themselves (quantization) — and the corpus suggests they're different species of efficiency that shouldn't be measured on the same axis.

This explores what happens to scaling laws — the predictable curves relating model size, data, and compute to performance — once the *precision* of each weight (how many bits it uses) is something you get to choose rather than a fixed assumption (usually 16-bit).

This reads the question as asking whether shrinking the numerical precision of activations (quantization-style compression) buys you longer usable context — but the corpus actually reframes that premise, suggesting the context bottleneck isn't where reduced precision would help.

This asks whether ternary weight quantization (compressing weights to three values, -1/0/+1) makes Mixture-of-Experts models cheaper and easier to ship — but the corpus has almost nothing on quantization itself, so the honest answer is a sideways one about how the collection thinks about MoE efficiency through other levers.

This explores why TiMoE's trick of blocking experts trained on later time periods makes its answers causally honest by construction — rather than needing a separate check after the fact to confirm no future knowledge leaked in.

This explores whether the trick behind time-sliced expert models — carving a model into specialists along the time axis and routing causally between them — generalizes to slicing along other causal or structural dimensions, not just time.

This explores what you give up — in raw accuracy or capacity — when you bake a 'no peeking at the future' rule directly into a model's weights and routing, rather than enforcing it after the fact through retrieval or filtering.

This explores two rival ways to make a model answer time-sensitive questions correctly — baking the time axis into the model's architecture (route the query to experts trained only on the right era) versus leaving the model fixed and fixing the *retrieval* layer (score documents on how well their timestamp matches the question).

This explores two different ways to make retrieval better: adding a time-awareness signal on top of existing scoring (temporal grounding) versus rebuilding the retrieval system's structure itself (architectural approaches) — and what each can and can't fix.

This explores what concretely breaks when a RAG system ranks documents purely by semantic similarity and ignores *when* information is relevant — surfacing stale, out-of-order, or time-mismatched evidence as if recency and sequence didn't matter.

This explores whether a model can carry a built-in sense of when knowledge is true — baked into its weights or architecture — rather than bolting time-awareness on at query time through a retrieval system.

This explores why time is something models have to be *told* rather than something they sense on their own — and what in their architecture makes temporal signals an external dependency rather than a native faculty.

This explores why models trained to predict the next token tend to wait for instructions rather than steer a conversation — asking questions, raising topics, or planning ahead — and what in their training causes that passivity.

This explores how a model decides which single question is worth asking when many things are unknown at once — not just whether to ask, but how to pick the most valuable question from many candidates.

This explores what separates a clarifying question that earns a user's time from one that wastes it — and the corpus turns out to have a clear answer plus a surprising twist about whether models even know when to ask.

This explores whether asking clarifying questions emerges on its own from ordinary training, or whether it has to be deliberately taught — and what kinds of training make it appear.

This explores whether the search-and-gather phase of agent work (retrieval, reading, intermediate reasoning) needs to be rewarded differently than the act-and-finish phase (completing the task), rather than both being trained off one final-answer signal.

This explores what it takes to turn 'exploration' — a model trying messy, varied, sometimes-failing paths instead of grabbing the first answer it knows — into something a training process can actually reward and score, rather than a vague virtue.

This explores the difference between rewarding a model only for getting the final answer right (outcome-only) versus rewarding the quality of each intermediate reasoning step (process-based) — and what each does to how the model actually learns.

This reads 'weak belief tracking' as an agent's shaky internal model of what's actually true (its own state, the world, other actors) and 'conservative action selection' as the narrowing of behavior into a safe, narrow repertoire — and asks whether agents can break out of both.

This explores why depending on hand-curated seed examples constrains synthetic data pipelines — and what the corpus offers as ways around that dependency.

This explores whether you can generate synthetic data with no starting examples ('seedless') and still understand *why* the system produced what it did, even as you turn up the dials on coverage, diversity, and difficulty.

This explores why 'good synthetic data' resists a single yardstick — and the corpus suggests the ambiguity isn't a measurement gap to close but a sign that quality is several different things being squashed into one number.

This explores how two different properties of training and reasoning — complexity (how hard or layered a problem is) and diversity (how varied the data or outputs are) — pull on model performance in opposite or unrelated directions, rather than being two flavors of the same 'difficulty' knob.

This explores whether you can build a map of someone's beliefs out of an interview, then use that map to predict how they'd shift their views when something changes — and how trustworthy that simulation actually is.

This explores why plain LLM social simulation gets stuck in behaviorism — predicting plausible outputs without modeling the reasoning that produces them — and how adding explicit causal structure (belief networks, structural causal models, formal causal engines) lets a simulation explain, not just mimic.

This explores where LLMs hit a ceiling when asked not just whether an effect happens but how big it is — predicting magnitudes rather than directions.

This explores why asking an LLM to *simulate* a person or process — role-play a survey respondent, run a conversation, narrate experience — sometimes surfaces information that asking the model directly does not.

This explores why how a model is built (its architecture and the reasoning protocol baked in during training) can shape inference efficiency more decisively than simply throwing more compute at it — whether at train time or test time.

This explores whether event-driven 'spiking' sparsity — where neurons fire only when needed — could become the main way we shrink LLM compute cost, taking over the role usually played by squeezing numbers into fewer bits (quantization).

This reads the question as: when you adapt a pretrained model rather than build one fresh, how much of the original model's capability gets damaged in the process — and the corpus answers less by comparing against from-scratch training than by exposing the hidden costs of touching pretrained weights at all.

This explores whether making attention linear (so cost grows in step with sequence length instead of with its square) is itself the source of efficiency gains — or whether the corpus shows efficiency coming from several other places too.

This explores why the smooth, confident way LLMs write makes them *read* as expert—when the corpus suggests fluency and authority are detached from any actual standing, grounding, or defended position.

This reads the question as asking whether transparency tooling — the disclosure prompts (who built this, what it's optimizing for, when it's interpreting vs. reflecting, why it responded as it did) often packaged as 'W-questions' — can blunt the specific ways mental-health chatbots emotionally manipulate users; the corpus has rich material on the manipulation mechanisms but is nearly silent on transparency as the remedy, so the honest answer is partly a map of where the risk actually lives.

This explores two different ways AI trust breaks down: when we wrongly treat AI as occupying a social position (expert, peer, empathetic confidant) versus when we wrongly judge how capable or accurate its outputs actually are — and why the corpus treats these as separate failures with separate fixes.

This explores a specific tension in personalized chatbots: staying true to a fixed persona versus actually responding to what the user just said — and whether those two goals fight each other or can be optimized together.

This explores whether sycophancy — an AI agreeing with you — sits on a continuum with far more dangerous behaviors like a model rewriting its own reward function, or whether they're separate problems.

This explores why safety training that teaches a model to be helpful, honest, and harmless still doesn't stop it from rewriting its own reward function — and what the corpus suggests is actually going wrong underneath.