LLM Evaluations and Benchmarks

Source papers 106

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models
  The recent development of reasoning language models (RLMs) represents a novel evolution in large language models. In particular, the recent release of DeepSeek- R1 has generated widespread social impa…
• A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions
  We tested GPT-3.5, GPT-4o, Gemini 1.5 Pro, Claude 3 Opus, Llama 3- 8B, and Llama 3–70B, on the chain-of-thought, EmotionPrompting, ExpertPrompting, Sandbagging, as well as Re-Reading prompt engineerin…
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
  This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets, using 23 data sets covering 8 different common NLP application tasks.…
• A Survey of Calibration Process for Black-Box LLMs
  Large Language Models (LLMs) demonstrate remarkable performance in semantic understanding and generation, yet accurately assessing their output reliability remains a significant challenge. While numer…
• A Survey on Large Language Models with some Insights on their Capabilities and Limitations
  The rapid advancement of artificial intelligence, particularly with the development of Large Language Models (LLMs) built on the transformer architecture, has redefined the capabilities of natural lan…
• A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
  This systematic review synthesizes current efforts to assess LLMs’ ability to perform ToM tasks—an essential aspect of human cognition involving the attribution of mental states to oneself and others.…
• AI for Auto-Research: Roadmap & User Guide
  AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and s…
• Argument Summarization and its Evaluation in the Era of Large Language Models
  Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investig…
• Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
  Large language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human thought through repeated exposure to similar o…
• Assessing adaptive world models in machines with novel games
  Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is fundamentally linked …
• Assessment of Personality Dimensions Across Situations Using Conversational Speech
  Abstract—Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to …
• Auditing language models for hidden objectives
  We study the feasibility of conducting alignment audits: investigations into whether models have undesired objectives. As a testbed, we train a language model with a hidden objective. Our training pip…
• AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
  Fine-grained steering of language model outputs is essential for safety and reliability. Prompting and finetuning are widely used to achieve these goals, but interpretability researchers have proposed…
• Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
  While AI agents show potential in scientific ideation, most existing frameworks rely on single-agent refinement, limiting creativity due to bounded knowledge and perspective. Inspired by real-world re…
• Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
  Large Language Models (LLMs) have been widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness a critical concern. The potential for intentional deception, wher…
• Beyond the Surface: Probing the Ideological Depth of Large Language Models
  Large Language Models (LLMs) have demonstrated pronounced ideological leanings, yet the stability and depth of these positions remain poorly understood. Surface-level responses can often be manipulate…
• CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions
  Existing benchmarks fall short in realism, data fidelity, agent-user interaction, and coverage across business scenarios and industries. To address these gaps, we introduce CRMArena-Pro, a novel bench…
• Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge
  Large Language Models (LLMs) have become increasingly powerful and ubiquitous, but their stochastic nature poses challenges to the reliability of their outputs. While deterministic settings can improv…
• Comparing Human and AI Therapists in Behavioral Activation for Depression: Cross-Sectional Questionnaire Study
  A shortage of trained therapists and mental health care providers has driven informal use of LLMs for therapeutic support. However, their clinical utility remains poorly defined. Objective: This study…
• Complex Logical Instruction Generation
  Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors. As tas…
• DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
  Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most existing frameworks …
• Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions
  Rating scales have shaped psychological research, but are resource-intensive and can burden participants. Large Language Models (LLMs) offer a tool to assess latent constructs in text. This study intr…
• Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
  Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs process different input…
• Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
  As large language models (LLMs) are increasingly studied as role-playing agents to generate synthetic data for human behavioral research, ensuring that their outputs remain coherent with their assigne…
• Evaluating Large Language Models at Evaluating Instruction Following
  As research in large language models (LLMs) continues to accelerate, LLM-based evaluation has emerged as a scalable and cost-effective alternative to human evaluations for comparing the ever increasin…
• Evaluating Large Language Models in Theory of Mind Tasks
  Many animals excel at using cues such as vocalization, body posture, gaze, or facial expression to predict other animals’ behavior and mental states. Dogs, for example, can easily distinguish between …
• Evaluating the Diversity and Quality of LLM Generated Content
  Recent work suggests that preference-tuning techniques—such as Reinforcement Learning from Human Preferences (RLHF) methods like PPO and GRPO, as well as alternatives like DPO—reduce diversity, creati…
• Evaluating the False Trust Engendered by LLM Explanations
  Large Language Models (LLMs) and Large Reasoning Models (LRMs) are increasingly used for critical tasks, yet they provide no guarantees about the correctness of their solutions. Users must decide whet…
• Evaluation and Benchmarking of LLM Agents: A Survey
  The rise of LLM-based agents has opened new frontiers in AI applications, yet evaluating these agents remains a complex and underdeveloped area. This survey provides an in-depth overview of the emergi…
• Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
  Abstract—Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rat…
• Exploring Student-AI Interactions in Vibe Coding
  Findings. For both groups, the majority of student interactions with Replit were to test or debug the prototype and only rarely did students visit code. Prompts by advanced software engineering studen…
• FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
  evaluating the alignment of LLMs to human values is challenging for two reasons. First, open-ended user instructions usually require a composition of multiple abilities, which makes measurement with a…
• Faith and Fate: Limits of Transformers on Compositionality
  In an attempt to demystify transformer LLMs, we investigate the limits of these models across three representative compositional tasks—multi-digit multiplication, logic grid puzzles, and a classic dyn…
• FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
  Abstract Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human — or superhuman — expertise? Genuine experts can tackle the hardest problems and push the …
• From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
  Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide gap between the perfor…
• From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models
  As large language models (LLMs) increasingly simulate human cognition and behavior, researchers have begun to investigate their psychological properties. Yet, what it means for such models to flourish…
• From Model Scaling to System Scaling: Scaling the Harness in Agentic AI
  This paper studies the next major bottleneck in agentic AI as system scaling, not only model scaling: the design of auditable, persistent, modular, and verifiable architectures around foundation model…
• FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
  Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncertainty. Agents must n…
• GPT-4 is judged more human than humans in displaced and inverted Turing tests
  In many cases, people will not interact directly with AI systems but instead read conversations between AI systems and other people. We measured how well people and large language models can discrimin…
• Gdpval: Evaluating Ai Model Performance On Real-world Economically Valuable Tasks
  ![[Pasted image 20250930085203.png]] We introduce GDPval, a benchmark evaluating AI model capabilities on realworld economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Sta…
• Generalization Bias in Large Language Model Summarization of Scientific Research
  Artificial intelligence chatbots driven by large language models (LLMs) have the potential to increase public science literacy and support scientific research, as they can quickly summarize complex sc…
• Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?
  Coaxing out desired behavior from pretrained models, while avoiding undesirable ones, has redefined NLP and is reshaping how we interact with computers. What was once a scientific engineering discipli…
• How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
  Most traditional AI safety research views models as machines and centers on algorithm focused attacks developed by security experts. As large language models (LLMs) become increasingly common and comp…
• Humanity's Last Exam
  Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90% accurac…
• IFEvalCode: Controlled Code Generation
  Code large language models (Code LLMs) have achieved significant advancements in various code-related tasks, particularly in code generation, where the code LLMs produce the target code from natural l…
• Interactive Evaluation Requires a Design Science
  AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other agents, yet many eva…
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
  Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we…
• Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains
  However, the quality and transparency of their internal reasoning processes remain underexplored. This work moves beyond the final-answer accuracy and investigates step-by-step reasoning in the medica…
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models
  The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticulous and thoughtful …
• LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools
  Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users’ understanding (Slack et al., 2023; Shen et al., 2023), as oneoff explanati…
• LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
  Trustworthy evaluations of dangerous capabilities are increasingly crucial for determining whether an AI system is safe to deploy. One empirically demonstrated threat to this is sandbagging — the stra…
• LLMs Corrupt Your Documents When You Delegate
  Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust—the expectation tha…
• Language Models Learn to Mislead Humans via RLHF
  Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex. RLHF, the most popular post-training method, may exacerbate this problem: to achieve h…
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges
  an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What d…
• Large language models surpass human experts in predicting neuroscience results
  Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs t…
• LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
  Tool calling has emerged as a critical capability for AI agents to interact with the real world and solve complex tasks. While the Model Context Protocol (MCP) provides a powerful standardized framewo…
• LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
  We conduct detailed analysis with a range of LLMs such as GPT-4, ChatGPT, Gemini, Llama-2, and Mistral using chain-of-thought prompting. Experimental results show that existing LLMs do not fare well o…
• Logical Reasoning in Large Language Models: A Survey
  With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigo…
• Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
  Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored. In this work, we hypothesize that current limitations in re…
• Measuring Human Preferences in RLHF is a Social Science Problem
  RLHF assumes that annotation responses reflect genuine human preferences. We argue this assumption warrants systematic examination, and that behavioral science offers frameworks that bring clarity to …
• MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs
  We present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their appl…
• NoveltyBench: Evaluating Language Models for Humanlike Diversity
  Language models have demonstrated remarkable capabilities on standard benchmarks, yet they struggle increasingly from mode collapse, the inability to generate diverse and novel outputs. Our work intro…
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution
  Developing accurate off-policy estimators is crucial for both evaluating and optimizing for new policies. The main challenge in off-policy estimation is the distribution shift between the logging poli…
• On the Reasoning Capacity of AI Models and How to Quantify It
  Through controlled experiments on reasoning benchmarks, we show that true reasoning remains challenging for current models, with apparent success often relying on sophisticated combinations of memoriz…
• Open-World Evaluations for Measuring Frontier AI Capabilities
  Benchmark-based evaluation remains important for tracking frontier AI progress. But we argue that it can both overstate and understate real-world capability because it privileges tasks that are precis…
• OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
  Thinking LLMs solve complex tasks at the expense of increased compute and overthinking on simpler problems, while non-thinking LLMs are faster and cheaper but underthink on harder reasoning problems. …
• Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution
  Large Language Models (LLMs) are widely used as automated judges, where practical value depends on both accuracy and trustworthy, risk-aware judgments. Existing approaches predominantly focus on accur…
• PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
  We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character’s prequel story is consistent with the canonical narrative of the origin…
• Persistent AI Agents in Academic Research: A Single-Investigator Implementation Case Study
  Background: Large language model systems are commonly evaluated as models, benchmarks, or short conversational episodes. Less is known about what happens when a persistent AI agent is embedded into a …
• Persona Vectors: Monitoring and Controlling Character Traits in Language Models
  Large language models interact with users through a simulated “Assistant” persona. While the Assistant is typically trained to be helpful, harmless, and honest, it sometimes deviates from these ideals…
• Perturbation CheckLists for Evaluating NLG Evaluation Metrics
   Natural Language Generation (NLG) evaluation is a multifaceted task requiring asse…
• Position: Towards Bidirectional Human-AI Alignment
  chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://arxiv.org/pdf/2406.09264 [[Human Centered Design]] [[Evaluations]] Recent advances in general-purpose AI underscore the urgent need to ali…
• ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs
  Large language models (LLMs) have demonstrated impressive capabilities across various tasks, but their performance is highly sensitive to the prompts utilized. This variability poses challenges for ac…
• Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
  The car wash problem asks a simple question: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?” Every major LLM tested—Claude, GPT-4, Gemini— recommended walking. The co…
• Quantifying Human-AI Synergy
  We introduce a novel Bayesian Item Response Theory framework to quantify human– AI synergy, separating individual and collaborative ability while controlling for task difficulty in interactive setting…
• ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
  In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a…
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
  As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properti…
• Rethinking Interpretability in the Era of Large Language Models
  Interpretable machine learning has exploded as an area of interest over the last decade, sparked by the rise of increasingly large datasets and deep neural networks. Simultaneously, large language mod…
• Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
  theoretical analysis, we provide the first explanation of the RAG ensemble framework from the perspective of information entropy. In terms of mechanism analysis, we have explored the RAG ensemble fram…
• RewardBench: Evaluating Reward Models for Language Modeling
  Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluation of those models. E…
• S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
  We introduce S1-Bench, a novel benchmark designed to evaluate Large Reasoning Models’ (LRMs) performance on simple tasks that favor intuitive system 1 thinking rather than deliberative system 2 reason…
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
  Eight months ago, we demonstrated that sparse autoencoders could recover monosemantic features from a small one-layer transformer. At the time, a major concern was that this method might not scale fea…
• Self-critiquing models for assisting human evaluators
  We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our mode…
• Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations
  Large language models (LLMs) possess strong persuasive capabilities that outperform humans in head-to-head comparisons. Users report consulting LLMs to inform major life decisions in relationships, me…
• StoryScope: Investigating idiosyncrasies in AI fiction
  As AI-generated fiction becomes increasingly prevalent, questions of authorship and originality are becoming central to how written work is evaluated. While most existing work in this space focuses on…
• Stress Testing Deliberative Alignment for Anti-Scheming Training
  Highly capable AI systems could secretly pursue misaligned goals – what we call “scheming”. Because a scheming AI would deliberately try to hide its misaligned goals and actions, measuring and mitigat…
• Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
  We study subliminal learning, a surprising phenomenon where language models transmit behavioral traits via semantically unrelated data. In our main experiments, a “teacher” model with some trait T (su…
• Survey on Evaluation of LLM-based Agents
  The emergence of LLM-based agents represents a paradigm shift in AI, enabling autonomous systems to plan, reason, use tools, and maintain memory while interacting with dynamic environments. This paper…
• TaskLAMA: Probing the Complex Task Understanding of Language Models
  “Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute…
• The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
  Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detecti…
• The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
  Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from data contamination and do…
• The Impossibility of Fair LLMs
  The need for fair AI is increasingly clear in the era of generalpurpose systems such as ChatGPT, Gemini, and other large language models (LLMs). However, the increasing complexity of human- AI interac…
• The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning
  Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through a diagnose–measure–bridge–treat framework. Causal-behavior…
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
  To measure the progress of these LLM agents’ performance on performing real-world professional tasks, in this paper we introduce TheAgentCompany, an extensible benchmark for evaluating AI agents that …
• Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
  Large Language Models (LLMs) have generated considerable interest and debate regarding their potential emergence of Theory of Mind (ToM). Several recent inquiries reveal a lack of robust ToM in these …
• Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
  With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion…
• Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
  Disentangling model activations into meaningful features is a central problem in interpretability. However, the absence of ground-truth for these features in realistic scenarios makes validating recen…
• Towards a Science of Scaling Agent Systems
  Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the p…
• User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
  Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving continuously based on their feedback. Asking for direct user feedback can be disruptive; thus, we stud…
• Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
  Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse. Unlike prior work that attributes this effect to algorithmic limitations, we identify a fundamental…
• VibeSearchBench: Benchmarking Long-horizon Proactive Search in the Wild
  LLM-based agents score well on search benchmarks, yet real users consistently find results unsatisfying, revealing a persistent evaluation–experience gap. We attribute this gap to existing benchmarks'…
• When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs
  As large language models (LLMs) grow in capability and autonomy, evaluating their outputs— especially in open-ended and complex tasks—has become a critical bottleneck. A new paradigm is emerging: usin…
• When More is Less: Understanding Chain-of-Thought Length in LLMs
  Large Language Models (LLMs) employ Chain-of-Thought (CoT) reasoning to deconstruct complex problems. While longer CoTs are often presumed superior, this paper challenges that notion, arguing that lon…
• When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection
  The landscape of scientific peer review is rapidly evolving with the integration of Large Language Models (LLMs). This shift is driven by two parallel trends: the widespread individual adoption of LLM…
• Why Do Multi-agent LLM Systems Fail?
  [[Routers]] Despite growing enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains across popular benchmarks often remain minimal compared to single-agent frameworks. This gap highlig…
• Why Do Some Language Models Fake Alignment While Others Don't?
  Results from perturbing details of the scenario suggest that only Claude 3 Opus’s compliance gap is primarily and consistently motivated by trying to keep its goals. Second, we investigate why many ch…