LAMMI-Pathology: A Tool-Centric Bottom-Up LVLM-Agent Framework for Molecularly Informed Medical Intelligence in Pathology
arXiv:2602.18773v1 Announce Type: new Abstract: The emergence of tool-calling-based agent systems introduces a more evidence-driven paradigm for pathology image analysis in contrast to the coarse-grained text-image diagnostic approaches. With the recent large-scale experimental adoption of spatial transcriptomics technologies, molecularly validated...
DREAM: Deep Research Evaluation with Agentic Metrics
arXiv:2602.18940v1 Announce Type: new Abstract: Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent benchmarks propose distinct methodologies, yet they suffer...
(Perlin) Noise as AI coordinator
arXiv:2602.18947v1 Announce Type: new Abstract: Large scale control of nonplayer agents is central to modern games, while production systems still struggle to balance several competing goals: locally smooth, natural behavior, and globally coordinated variety across space and time. Prior approaches...
Benchmark Test-Time Scaling of General LLM Agents
arXiv:2602.18998v1 Announce Type: new Abstract: LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests. While existing benchmarks focus on domain-aware environments for developing specialized agents, evaluating general-purpose agents requires more realistic settings that...
Evaluating Large Language Models on Quantum Mechanics: A Comparative Study Across Diverse Models and Tasks
arXiv:2602.19006v1 Announce Type: new Abstract: We present a systematic evaluation of large language models on quantum mechanics problem-solving. Our study evaluates 15 models from five providers (OpenAI, Anthropic, Google, Alibaba, DeepSeek) spanning three capability tiers on 20 tasks covering derivations,...
Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing
arXiv:2602.19160v1 Announce Type: new Abstract: This paper examines the reasoning capabilities of Large Language Models (LLMs) from a novel perspective, focusing on their ability to operate within formally specified, rule-governed environments. We evaluate four LLMs (Gemini 2.5 Pro and Flash...
Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training
arXiv:2602.19225v1 Announce Type: new Abstract: Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately distinguishing high-value informative signals from stochastic noise is critical for sample-efficient training. In real-world...
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
arXiv:2602.18806v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate strong reasoning performance, yet their ability to reliably monitor, diagnose, and correct their own errors remains limited. We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle...
Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation
arXiv:2602.18966v1 Announce Type: new Abstract: Domain-specific speech remains a persistent challenge for automatic speech recognition (ASR), even for state-of-the-art systems like OpenAI's Whisper. We introduce Whisper: Courtside Edition, a novel multi-agent large language model (LLM) pipeline that enhances Whisper transcriptions...
Construct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman Problem
arXiv:2602.23579v1 Announce Type: new Abstract: The Multiple Traveling Salesman Problem (mTSP) extends the Traveling Salesman Problem to m tours that start and end at a common depot and jointly visit all customers exactly once. In the min-max variant, the objective...
AI Must Embrace Specialization via Superhuman Adaptable Intelligence
arXiv:2602.23643v1 Announce Type: new Abstract: Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is...
ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation
arXiv:2602.23716v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents show promise for e-commerce conversational shopping, yet existing implementations lack the interaction depth and contextual breadth required for complex product research. Meanwhile, the Deep Research paradigm, despite advancing information synthesis...
Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction
arXiv:2602.24080v1 Announce Type: new Abstract: The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet unanswered question is whether they can converse like humans. To tackle this, we...
Recycling Failures: Salvaging Exploration in RLVR via Fine-Grained Off-Policy Guidance
arXiv:2602.24110v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the complex reasoning capabilities of Large Reasoning Models. However, standard outcome-based supervision suffers from a critical limitation that penalizes trajectories that...
Higress-RAG: A Holistic Optimization Framework for Enterprise Retrieval-Augmented Generation via Dual Hybrid Retrieval, Adaptive Routing, and CRAG
arXiv:2602.23374v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into enterprise knowledge management systems has been catalyzed by the Retrieval-Augmented Generation (RAG) paradigm, which augments parametric memory with non-parametric external data. However, the transition from proof-of-concept to...
Now You See Me: Designing Responsible AI Dashboards for Early-Stage Health Innovation
arXiv:2602.23378v1 Announce Type: cross Abstract: Innovative HealthTech teams develop Artificial Intelligence (AI) systems in contexts where ethical expectations and organizational priorities must be balanced under severe resource constraints. While Responsible AI practices are expected to guide the design and evaluation...
Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages
arXiv:2602.23388v1 Announce Type: cross Abstract: The rising demand for inclusive speech technologies amplifies the need for multilingual datasets for Natural Language Processing (NLP) research. However, limited awareness of existing task-specific resources in low-resource languages hinders research. This challenge is especially...
Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking
arXiv:2603.00267v1 Announce Type: new Abstract: Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns...
TraderBench: How Robust Are AI Agents in Adversarial Capital Markets?
arXiv:2603.00285v1 Announce Type: new Abstract: Evaluating AI agents in finance faces two key challenges: static benchmarks require costly expert annotation yet miss the dynamic decision-making central to real-world trading, while LLM-based judges introduce uncontrolled variance on domain-specific tasks. We introduce...
AI Runtime Infrastructure
arXiv:2603.00495v1 Announce Type: new Abstract: We introduce AI Runtime Infrastructure, a distinct execution-time layer that operates above the model and below the application, actively observing, reasoning over, and intervening in agent behavior to optimize task success, latency, token efficiency, reliability,...
DenoiseFlow: Uncertainty-Aware Denoising for Reliable LLM Agentic Workflows
arXiv:2603.00532v1 Announce Type: new Abstract: Autonomous agents are increasingly entrusted with complex, long-horizon tasks, ranging from mathematical reasoning to software generation. While agentic workflows facilitate these tasks by decomposing them into multi-step reasoning chains, reliability degrades significantly as the sequence...
EMPA: Evaluating Persona-Aligned Empathy as a Process
arXiv:2603.00552v1 Announce Type: new Abstract: Evaluating persona-aligned empathy in LLM-based dialogue agents remains challenging. User states are latent, feedback is sparse and difficult to verify in situ, and seemingly supportive turns can still accumulate into trajectories that drift from persona-specific...
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs
arXiv:2603.00578v1 Announce Type: new Abstract: Long chain-of-thought~(CoT) has become a dominant paradigm for enhancing the reasoning capability of large reasoning models~(LRMs); however, the performance gains often come with a substantial increase in reasoning budget. Recent studies show that existing CoT...
MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
arXiv:2603.00808v1 Announce Type: new Abstract: A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons with collective awareness, without centralized supervision or explicit communication. In this...
MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
arXiv:2603.00873v1 Announce Type: new Abstract: With the increasing demand for step-wise, cross-modal, and knowledge-grounded reasoning, multimodal large language models (MLLMs) are evolving beyond the traditional fixed retrieve-then-generate paradigm toward more sophisticated agentic multimodal retrieval-augmented generation (MM-RAG). Existing benchmarks, however, mainly...
HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents
arXiv:2603.00977v1 Announce Type: new Abstract: Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon tasks that require structured planning and reliable execution. Existing approaches predominantly rely on flat autoregressive...
CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration
arXiv:2603.00993v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular. However, current single-LLM evaluation approaches face significant challenges, including inconsistent judgments and inherent biases from pre-training data. To address...
DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
arXiv:2603.01106v1 Announce Type: new Abstract: Reinforcement learning (RL) with group relative policy optimization (GRPO) has become a widely adopted approach for enhancing the reasoning capabilities of multimodal large language models (MLLMs). While GRPO enables long-chain reasoning without a critic, it...
Embracing Anisotropy: Turning Massive Activations into Interpretable Control Knobs for Large Language Models
arXiv:2603.00029v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit highly anisotropic internal representations, often characterized by massive activations, a phenomenon where a small subset of feature dimensions possesses magnitudes significantly larger than the rest. While prior works view these...
GRIP: Geometric Refinement and Adaptive Information Potential for Data Efficiency
arXiv:2603.00031v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is increasingly governed by data efficiency rather than raw scaling volume. However, existing selection methods often decouple global distribution balancing from local instance selection, compromising the hierarchical integrity...