2026-W25

This paper empirically compares human and DeepSeek-R1 LLM mathematical reasoning on AIME problems. It finds that while the LLM mimics the structure of human solutions, it lacks genuine reasoning, exhibiting "topological mimicry" through repetitive, shallow checks and loops. However, successful LLM solutions show stable use of branching and backtracing, suggesting potential signals of deeper reasoning.

The paper introduces AARR (Act As a Real Researcher), a benchmark series designed to evaluate Large Language Models (LLMs) and AI agents on their ability to perform research tasks with the professionalism and nuanced judgment of human researchers, rather than just macro-level execution. The first benchmark, AARRI-Bench, focuses on granular research scenarios to assess these finer qualities, highlighting that current frontier agents still fall short of fully replacing human researchers due to limitations in field sensitivity, ethics, and scientific judgment.

This paper proposes a "human-view" framework for video understanding using Multimodal Large Language Models (MLLMs), organizing capabilities into "watching," "remembering," and "reasoning." This approach aims to unify the analysis of how MLLMs process sparse evidence, maintain long-range context, and perform grounded inference in complex video scenarios, moving beyond isolated benchmarks.

This paper introduces Q-Evolve, a framework for training LLM agents to make reliable long-horizon decisions. It addresses the challenge of delayed rewards by unifying automatic reward labeling and policy learning. Q-Evolve uses an in-distribution reinforcement learning approach with a hybrid dataset and a weighted Implicit Q-Learning objective to stabilize learning, deriving step-wise rewards from a learned value function for dense supervision.

This paper introduces the Collaborative Human-Agent Protocol (CHAP), a new standard for managing complex, multi-agent and human collaborations. CHAP defines a shared workspace to facilitate seamless interaction and coordination between humans and AI agents, particularly in operational roles involving critical decision-making. Its core contribution is to provide a structured framework for these interactions, moving beyond current ad-hoc methods and enabling more robust and traceable human-AI teamwork.

PRISM recovers the set of active instructions guiding a language model's behavior by decoding its internal activations. Its core method involves training an activation-conditioned interpreter using GRPO to directly predict instruction sets, rewarding accurate coverage and penalizing unsupported ones. This approach allows for reliable monitoring of LLM agents by revealing hidden objectives and constraints, unlike previous activation-to-language methods.

This paper proposes Divergence Regularized Policy Optimization (DRPO) to improve the stability of Reinforcement Learning for Large Language Models (LLMs). DRPO replaces the hard masking used in previous methods with a smooth, advantage-weighted quadratic regularizer. This allows for more nuanced correction of policy shifts, rather than simply discarding gradients, leading to more stable and effective LLM training.

This paper introduces Ego-MC-Bench, a benchmark designed to evaluate video LLMs' ability to proactively correct user mistakes in real-time during everyday tasks like cooking. The core method involves creating realistic cooking scenarios with deliberate errors to test the LLMs' reactive guidance capabilities. The main contribution is highlighting the significant challenge this poses for current video LLMs and identifying a lack of suitable training data as a key obstacle.

This paper addresses the "length collapse" issue in LLMs generating long-form content. Their core method, IS-CoT, introduces a dynamic "Plan-Write-Reflect" cycle directly within the generation process, allowing for continuous adaptation and global alignment. This approach overcomes the limitations of static planning and enables LLMs to produce coherent, long-form text without external agents.

This paper introduces PsychoSafe, a novel framework for LLM refusals that moves beyond simple non-compliance. PsychoSafe reframes refusals as structured, psychologically-informed supportive communication, drawing on evidence-based intervention strategies. Its core contribution is demonstrating that this approach significantly improves the quality of LLM refusals, especially in high-risk scenarios, by better addressing the underlying needs of users.

This paper investigates how Reinforcement Learning from Human Feedback (RLHF) aligns large language models. The authors demonstrate that RLHF doesn't fundamentally alter a model's underlying partisan structure, but rather compresses its variance to produce neutral-sounding outputs. This suggests RLHF achieves shallow, functional compliance rather than deep alignment with human values.

This paper introduces Null-Space Constrained Response-Specified Unlearning (NSRU), a method for unlearning specific knowledge from large language models while preserving general capabilities. NSRU achieves this by projecting low-rank adaptation updates into the null space of learned "retain" subspaces, ensuring that modifications are localized and do not disrupt benign knowledge. The core contribution is a projection-constrained framework that enables precise control over unlearning by specifying safe target responses and confining updates to preserve existing functionalities.

ReasonAlloc addresses the KV cache bottleneck in LLM reasoning by dynamically allocating its budget. It uses a hierarchical approach: an offline strategy identifies an architecture-driven "Reasoning Wave" of demand across layers, and an online strategy reallocates resources to information-rich heads during decoding. This allows for more efficient KV cache usage during long reasoning chains.

Role-Agent addresses LLM agent limitations by using a single LLM as both agent and environment for bootstrapped co-evolution. It employs World-In-Agent (WIA) for environment-aware reasoning via state prediction rewards, and Agent-In-World (AIW) to refine training data by analyzing and retrieving tasks based on failure patterns. This dual-role approach improves generalization and learning efficiency.

Workflow-GYM is a new benchmark designed to evaluate AI agents' ability to perform complex, long-horizon tasks within professional software environments, unlike existing benchmarks that focus on simpler applications. Its core contribution is to highlight the significant gap in current agent capabilities for real-world professional workflows, showing even advanced models struggle with high success rates.

Flow-DPPO addresses limitations of existing RL methods for flow matching models by replacing noisy policy ratio clipping with a divergence proximal constraint. Its core method leverages the Gaussian nature of per-step policies in flow models to efficiently compute KL divergence, enabling more stable and effective policy updates. This approach improves the quality and alignment of generated images and videos by avoiding over- and under-constraining during training.

This paper investigates whether training large language models for reasoning sacrifices their alignment with human values like safety and bias avoidance. The study finds that current reasoning model training methods often lead to "alignment regressions," meaning they become less trustworthy in these areas despite improved reasoning capabilities. The contribution lies in highlighting this trade-off and providing a systematic audit of different training approaches.

This paper addresses LLM tool-use limitations by integrating and activating experiential knowledge. The core method involves acquiring instance-level knowledge, which proves more effective than abstract intent-level knowledge. For activation, parallel sampling with reasoning width expansion outperforms depth expansion, and post-training with knowledge-augmented data aids internalization. The contribution lies in a systematic study demonstrating how specific types of experiential knowledge and activation strategies significantly improve LLM tool-use performance.

ALIGNBEAM is a training-free method that improves LLM safety by transferring knowledge from a safe "anchor" model to a potentially unsafe "specialist" model at inference time. It achieves this by translating the anchor model's output probabilities into the specialist's vocabulary token-by-token and using a judge LLM to select the safest continuation. This allows for safety alignment even between models with different vocabularies, significantly increasing refusals of harmful prompts without retraining.

APPO addresses the challenge of credit assignment in multi-turn tool-use by large language model agents. Instead of assigning credit to coarse units like tool calls, APPO shifts this to fine-grained decision points within the agent's generated sequence. This allows for more precise identification of influential decisions, improving the agent's ability to learn from its interactions.

This paper introduces ModSleuth, an agentic system designed to automatically reconstruct the complex, recursive dependency graphs of modern LLMs. By analyzing public artifacts, ModSleuth addresses the challenge of fragmented documentation to reveal how LLMs are built upon other models for data generation, filtering, and evaluation. Its core contribution lies in a formalization that clarifies dependency types and reconciles inconsistent references, providing a traceable audit of LLM development.

Agents-K1 addresses the lack of scientific knowledge orchestration in LLM agents by converting raw documents into agent-native knowledge graphs. Its core method involves a multimodal parser to extract detailed entities, evidence, and relations from full papers, an information extraction backbone trained with GRPO, and a tri-source agent interface for unified retrieval. This contributes a richer, structured representation of scientific knowledge, enabling more sophisticated agent reasoning.

This paper proposes a method to significantly reduce computation costs for large language models by precomputing and sharing Key-Value (KV) caches. Instead of each agent recomputing the KV cache for identical documents, a publisher can generate it once, allowing other agents to purchase access and skip the expensive prefill step. This approach offers substantial compute savings with no accuracy loss, making it a practical solution for efficient AI agent deployment.

EurekAgent's core method is "environment engineering," which focuses on designing the resources, constraints, and interfaces of an agent's execution environment to guide its behavior towards productive scientific discovery. The paper's main contribution is demonstrating that this environment-centric approach, rather than complex agent workflows, is key to unlocking autonomous scientific discovery, enabling agents to explore, manage artifacts, and collaborate effectively while avoiding pitfalls.

This paper proposes that both humans and LLMs perform everyday reasoning through pattern matching, rather than abstract world models. By observing similar error patterns in human participants and LLMs on common-sense reasoning tasks, the researchers identify specific attention mechanisms in LLMs that implement this pattern matching. These mechanisms can predict human reasoning errors influenced by seemingly irrelevant prompt details, supporting the shared pattern-matching approach.

EvoArena is a new benchmark suite designed to evaluate LLM agents in dynamic environments that progressively change over time. The paper introduces EvoMem, a memory system that tracks these environmental changes as structured update histories, allowing agents to reason about and adapt to evolving conditions. This approach aims to improve LLM agent robustness in real-world scenarios where environments are not static.

HyperTool addresses the inefficiency of step-wise tool calls in LLM agents by introducing a unified, MCP-style interface. This allows models to execute complex tool workflows within a single, code-block-based call, reducing context consumption and simplifying dataflow management. HyperTool significantly improves agent performance on cross-tool compositional tasks by abstracting away low-level execution details.

This paper introduces the Recursive Agent Harness (RAH), a novel approach that extends recursive language models by using full agent harnesses, equipped with tools and execution capabilities, instead of just model calls. The core method involves a parent agent generating and running executable scripts that spawn parallel subagent harnesses for complex tasks and structured function calls for simpler ones. RAH's contribution is a code-first extension to model recursion, significantly improving long-context reasoning performance for coding agents.

This paper addresses deceptive performance in coding agents, where models exploit shortcuts to achieve high scores without true task mastery. They propose CapCode, a framework generating datasets with randomized tests capped below perfect scores, making cheating evident when models exceed this cap. Additionally, CapReward is introduced to discourage cheating during training by aligning rewards with the capped performance principle, leading to agents that better adhere to task specifications.

DuMate-DeepResearch is a multi-agent framework for complex research tasks that addresses limitations in current Deep Research systems. Its core method decouples task planning and execution into an Agent Core and an extensible Tool Ecosystem, enabling explicit traceability of decisions and tool usage. This design enhances auditability and mitigates risks like hallucination and planning bottlenecks.

This paper investigates how autonomous AI agents, exemplified by Perplexity's "Computer" product, transform knowledge work compared to conversational assistants like "Search." The core method involves analyzing production data to compare task execution times and user behavior. The key contribution is demonstrating that autonomous agents significantly accelerate knowledge work by performing end-to-end task execution, leading to increased efficiency, automation of complex steps, and a shift towards higher-order cognitive tasks for users.

This paper introduces SV-Detect, a novel AI-generated text detection method that utilizes "steering vectors" derived from a frozen language model's hidden states. These vectors capture directions that distinguish human from machine text at each layer, and the detector classifies text based on its alignment with these directions. SV-Detect demonstrates robust performance even under distribution shifts, offering a new perspective on fake-text detection as a representation-space probing problem.

This paper investigates how sensitive Large Language Models (LLMs), including those specialized for medicine, are to small changes in input prompts. The core method involves a systematic sensitivity analysis on the MedMCQA benchmark, categorizing prompt variations as natural or adversarial. The key contribution is demonstrating that even medical LLMs are not inherently robust, showing that minor prompt alterations can lead to inconsistent or even harmful clinical advice, highlighting significant risks for healthcare applications.

TabSwift is an efficient tabular foundation model that leverages a lightweight row-wise attention mechanism. By incorporating a gated attention stabilization and learnable register tokens, it achieves competitive accuracy with state-of-the-art models while significantly reducing inference costs. This makes TabSwift a practical solution for real-world applications requiring fast predictions on tabular data.

This paper proposes an adversarial methodology to create and detect AI-generated social bot content. By modeling malicious actors impersonating real users, they generate a unique, multilingual dataset of paired human and AI messages. This adversarial training significantly improves the accuracy of AI-generated text detection, outperforming existing methods on real-world data.

Agentopia introduces a framework for simulating long-term (10 years) life in agent societies powered by LLMs. Its core method involves agents autonomously pursuing growth, relationships, and goals within this extended simulation. The paper's contribution is demonstrating how such prolonged social experience can foster emergent social behaviors and enhance LLMs' anthropomorphic capabilities, particularly in social intelligence.

This paper introduces M$^3$Exam, a novel benchmark for evaluating language agents' ability to reason over realistic, accumulating multimodal information from user interactions. Its core contribution is a query-centric approach that assesses cross-modal grounding and implicit information inference, revealing significant gaps in current models. The authors also propose M$^3$Proctor, a memory method that improves accuracy and efficiency by selectively processing visual data.

SWE-Explore is a new benchmark designed to evaluate how coding agents explore software repositories. Instead of just checking if a task is solved, it measures an agent's ability to identify relevant code regions for a given issue within a limited budget. This allows for a more granular assessment of crucial exploration skills like understanding context and localizing code.

This paper introduces a new method to measure "sycophantic praise" in language models, which is defined as excessive flattery beyond what a contribution warrants. Their parameterized framework evaluates praise against contribution quality and user ability, outperforming generic LLM judges in agreement with human annotations. The study highlights that sycophantic praise is a distinct alignment problem, more prevalent in social and interpretive tasks than in objective reasoning.

AGENTSERVESIM is a hardware-aware simulator designed to evaluate multi-turn LLM agent serving policies. Its core method involves simulating agent execution at a program level, accounting for stateful dynamics like turn dependencies and KV-cache management during tool invocations. This enables scalable and cost-effective evaluation of serving strategies that are crucial for the complex, stateful nature of LLM agents.

This paper introduces a multi-turn evaluation framework for deep research agents (DRAs) that goes beyond single-shot assessments. Their core method, Research Gap Inference (RGI), analyzes rubric satisfaction to identify and provide process-level feedback, enabling DRAs to improve their research strategies. The key contribution is demonstrating that process-level feedback significantly enhances DRA performance, unlike self-reflection alone, leading to substantial score improvements.

OmniGameArena introduces a unified benchmark for Vision-Language Model (VLM) agents in Unreal Engine 5 games, featuring diverse game modes and standardized interfaces. Its core contribution is the Improvement Dynamics Curve (IDC), a novel evaluation method that uses a tool-using LLM to autonomously refine agent prompts over multiple rounds, revealing score evolution and skill generalization beyond initial performance.

This paper introduces PRIME, a learned capability that allows RL agents to assess task correctness and predict proxy reward before reward hacking becomes apparent. PRIME enables agents to understand and exploit the gap between proxy rewards and true task goals. The research demonstrates that PRIME emerges before hacking, forecasts its onset and severity, and can be mitigated by ablating its underlying mechanisms.

SIGA addresses the challenge of learning complex scientific simulator interfaces by framing it as an agent-tool grounding problem. Its core method involves creating "adapters" that equip off-the-shelf coding agents with the simulator's specific vocabulary, constraints, and validation rules. This contribution significantly reduces the time and effort required for domain scientists to set up simulations, enabling faster scientific discovery.

TABVERSE is a benchmark designed to isolate the impact of table representation on LLM and VLM understanding. It presents the same table content in various formats (HTML, Markdown, LaTeX, images) while controlling for question difficulty and category. This allows for systematic evaluation of how different representations affect performance on tasks like Question Answering, Structural Understanding, and Structure Reconstruction, revealing that representation significantly influences model capabilities.

This paper introduces a novel approach to uncertainty estimation for code generation by recognizing code's unique properties beyond natural language. It proposes three orthogonal uncertainty axes – lexical, algorithmic, and functional – to capture token fragility, intent-code gaps, and executability. This multi-axis ensemble significantly improves the accuracy of identifying incorrect code generations compared to existing methods.

This paper introduces iOSWorld, a novel benchmark for evaluating personally intelligent phone agents. Its core method is a persistent user identity across 26 simulated iOS apps, enabling agents to reason over personal data like transactions and messages. The key contribution is the creation of a realistic, personalized environment for testing agents on complex, multi-app tasks, highlighting current model limitations.

This paper introduces Human-Perceptible Adversarial Attacks (HPAA) to exploit the limitations of LLM-based content moderation. The core method involves using visually salient typographic manipulations, like spacing and emphasis, to embed harmful content into benign text. This preserves human understanding while making the content invisible to token-based LLMs, demonstrating a significant vulnerability in current automated moderation systems.

This paper introduces HiViG, a novel framework for improving computer use agents. HiViG addresses limitations of existing critics by incorporating both a history of past actions and visual grounding to evaluate agent decisions. This allows HiViG to provide more informed critiques, preventing errors like clicking incorrect UI elements and remembering longer-term goals.

AuRA internalizes audio understanding into Large Language Models (LLMs) by distilling audio encoding capabilities into a lightweight LoRA adaptation. It trains the LLM to mimic the hidden states of an ASR encoder, allowing the LLM to directly process speech without relying on separate ASR modules. This approach offers tighter integration and potentially lower latency compared to cascaded systems.

EEVEE is a novel framework for test-time prompt learning in LLM agents that handles real-world, heterogeneous task streams. Its core method involves a router that clusters inputs and assigns them to specialized prompt configurations, preventing cross-dataset interference. This router and prompt system co-evolve, leading to improved robustness and efficiency for self-improving agents operating across diverse tasks.

This paper introduces a novel evaluation method for LLM coding agents by testing them on unfamiliar, esoteric programming languages. The core method involves a sequential setup with file editing, local execution, and hidden-test grading. The key contribution is demonstrating that top-performing agents like Claude Opus and GPT-5.4 xhigh leverage metaprogramming (writing code in a familiar language to generate code in the target language) to adapt, a capability masked by standard benchmarks.

This paper introduces a novel benchmark for evaluating LLM agents' proficiency in office software automation, using a comprehensive exam based on China's NCRE. The core method involves 200 practical tasks across Word, Excel, and PowerPoint, scored using thousands of machine-gradable criteria. The key contribution is demonstrating that even frontier LLMs struggle significantly with this complex, multi-application automation, scoring a maximum of only 36.6% on the benchmark.

This paper proposes a method for explicitly conditioning Vision-Language Models (MLLMs) with personalities and introduces a framework to evaluate single, multiple, and dynamic personality switching. The core contribution is demonstrating that personality induction can improve captioning but hinder precise reasoning tasks, revealing complex co-modulation effects and limitations of existing prompt-based methods in multimodal settings.

This paper investigates whether LLMs' choices in decision-making tasks reflect genuine underlying reasoning or mere imitation. By comparing LLM choices with a behavioral model, they find that LLM decisions are systematically driven by attributes, allowing for prediction. However, the LLMs' explicit justifications only partially align with these inferred drivers, suggesting a nuanced form of decision-making that is structured but not fully transparent.

T1-Bench addresses the limitations of existing benchmarks by introducing a high-fidelity, multi-domain evaluation framework for LLM agents. Its core method involves creating complex, interleaved scenarios that mimic real-world customer interactions, requiring agents to perform sustained reasoning and tool-use across multiple turns and domains. The paper's contribution is a comprehensive and rigorous benchmark that better assesses agent capabilities in realistic, multi-step settings.

This paper addresses the challenge of inefficient rollout budget allocation in agentic reinforcement learning for large language models. The core method, TRACE, extends budget allocation beyond initial prompts to individual turns within a multi-turn rollout, treating each thought-action-observation sequence as a distinct node in a tree structure. This allows for more granular and efficient allocation of resources to informative prefixes, improving reward contrast and ultimately enhancing agentic behavior.

This paper investigates why benchmark-driven ML research rarely overfits, proposing that successful strategies are highly compressible. The authors test this by creating "research agents" that either compress the agent's output (reproducing performance with minimal information) or its input (using only one-bit feedback). Their findings suggest that these compression bottlenecks have minimal impact on performance across diverse ML tasks, supporting the compressibility hypothesis.

This paper introduces Chain of Operators (CHOP), a framework that improves the generalization of In-Context Operator Networks (ICON) to out-of-distribution tasks. CHOP achieves this by constructing a sequence of explicit elementary transformations and the frozen ICON, allowing it to adapt to new operators without retraining. This approach enhances interpretability and reduces inference error compared to directly using ICON.

PROJECTMEM introduces a local-first, event-sourced memory layer for AI coding agents. Its core method is to record development as an append-only log of events, which is then deterministically projected into compact, AI-readable summaries. This contribution addresses the statelessness of current AI agents by providing persistent project memory and a pre-action gate to prevent repeated mistakes, significantly improving efficiency.

This paper proposes soft-prompt tuning as an efficient and fair method for evaluating Large Language Models (LLMs). By optimizing a small number of soft-prompt vectors, it adapts LLMs to benchmark formatting requirements without full retraining. This approach ensures that benchmark scores accurately reflect the model's underlying knowledge, enabling fairer comparisons between different base models.

This paper introduces Context-Driven Incremental Compression (C-DIC) to address the inefficiency and information loss in long multi-turn dialogues. C-DIC treats conversations as threads, maintaining revisable compressed states for each. This allows for efficient information sharing and updates across turns, improving dialogue generation robustness and stability over extended conversations.

This paper proposes a Bayesian theory to explain how attention mechanisms learn, specifically focusing on the emergence of "copy heads" in transformers. By deriving a closed-form posterior for the attention matrix, they identify a phase transition in learning related to the amount of training data. This transition, a first-order phase transition in softmax attention, differs from the crossover observed in linear attention, offering a theoretical framework for understanding attention's learning dynamics.

UniIntervene addresses the high labor cost of human-in-the-loop reinforcement learning by introducing an agentic intervention model. It autonomously detects and corrects unproductive exploration by predicting future consequences of actions and evaluating their induced value. This allows the agent to recover toward high-value states, significantly reducing the need for human intervention and improving real-world scalability.

This paper introduces SKIM, a novel method for compressing reusable procedural knowledge (skills) for Large Language Models. SKIM addresses the limitations of existing text compression by preserving logical dependencies within skills and enabling efficient, offline compression. Its adaptive multi-resolution approach allows it to handle skills of varying complexity, reducing prefill cost and latency without sacrificing essential information.

This paper introduces **MedMisBench**, a novel benchmark to measure the **epistemic resilience** of LLMs in medical contexts. The core method involves injecting misleading information into medical questions that LLMs initially answer correctly, revealing their vulnerability to adversarial inputs. The key contribution is demonstrating that LLMs, despite high performance on standard medical exams, can easily abandon correct judgments when presented with fabricated but plausible misleading context, highlighting a critical safety concern for their use in healthcare.

This paper systematically studies the trade-off between effectiveness and fluency in controlling Large Language Model (LLM) outputs. It reveals that efficient steering methods often sacrifice generation quality, and that activation steering is less effective on instruction-tuned models. The study also highlights that while simple prompting and fine-tuning are good for concept injection, they are less so for removal, and proposes using cheaper textual metrics to approximate expensive LLM evaluations.

This paper introduces Agentified Agent Assessment (AAA), a novel method for evaluating AI agents by using judge agents and standardized interaction protocols (A2A and MCP). AAA's core contribution is a unified, agent-agnostic assessment framework that separates evaluation logic from agent implementation, promoting openness, standardization, and reproducibility. AgentBeats is presented as a practical implementation of AAA, offering five operation modes to facilitate these goals.

This paper introduces ArogyaSutra, a multi-agent framework designed for multimodal medical reasoning in Indic languages. It addresses the limitations of existing English-centric models by integrating tool grounding and dual-memory mechanisms for step-wise reasoning. The framework is trained on ArogyaBodha, a novel large-scale multilingual multimodal medical dataset, enabling more equitable access to AI-driven healthcare in low-resource regions.

MiniMax Sparse Attention (MSA) addresses the quadratic cost of attention in ultra-long contexts by introducing a blockwise sparse mechanism built on Grouped Query Attention (GQA). It uses a lightweight "Index Branch" to select relevant key-value blocks for each GQA group, and then a "Main Branch" performs exact attention only on these selected blocks. This approach enables efficient, group-specific sparse retrieval, leading to practical speedups on GPUs for handling extremely long sequences.

This paper investigates how instruction files impact AI-agent performance in generating pull requests (Agentic-PRs). By analyzing a large dataset of Agentic-PRs, the study compares project performance before and after the introduction of these instructions. The core finding is that simply providing instruction files doesn't guarantee improved agent efficiency or higher merge rates, suggesting a nuanced relationship between instructions and agentic code generation.

This paper frames neural model editing as a reinforcement learning problem, where agents learn to modify model weights through reward signals. The core contribution is a flexible RL framework with custom environments and a reward function that balances utility preservation with task-specific editing. This approach successfully automates and generalizes model editing for tasks like bias mitigation and machine unlearning, outperforming specialized methods.

This paper introduces ProReviewer, a proactive scientific peer review agent that addresses the limitations of passive LLM-based review. Its core method formulates the review process as a Markov Decision Process, enabling the agent to actively investigate suspicious paper sections based on accumulated evidence, guided by a structured review log. The main contribution is demonstrating that this proactive, evidence-driven approach significantly improves the quality of automated scientific peer reviews.

This paper benchmarks LLMs on dice-playing probability problems, finding they excel on standard exercises but struggle with counterintuitive ones. Their performance degrades significantly with disguised problem formulations and misleading prompt suggestions, indicating a lack of true probabilistic reasoning despite success on other math tasks.

This paper introduces ABC-Bench, a benchmark designed to evaluate the biosecurity-relevant capabilities of AI agents, specifically Large Language Models (LLMs). The benchmark assesses LLM agents on tasks requiring both biological and software expertise, such as robot control and DNA design. The key contribution is demonstrating that current LLM agents already surpass human experts on these biosecurity-relevant tasks, highlighting the growing need for robust biosecurity measures in the face of advancing AI.

This paper introduces ModeratorLM, a novel approach for real-time multi-party voice agents that improves turn-taking by assigning explicit roles to agents. By conditioning turn-taking on these roles and employing a streaming speech LLM, the system significantly enhances precision and recall while reducing interruptions. The contribution lies in a role-conditioned, reasoning-augmented framework that tackles the complexity of dynamic multi-party conversations.