BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
arXiv:2603.00876v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect, but also...
HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis
arXiv:2603.01121v1 Announce Type: new Abstract: While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and...
FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning
arXiv:2603.01135v1 Announce Type: new Abstract: Large Language Models have achieved remarkable success in language understanding and reasoning, and their multimodal extensions enable comprehension of images, video, and audio. Inspired by this, foundation models for brain functional connectivity networks derived from...
TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation
arXiv:2603.00025v1 Announce Type: new Abstract: Direct Preference Optimization is an offline post-SFT method for aligning language models from preference pairs, with strong results in instruction following and summarization. However, DPO's sequence-level implicit reward can be brittle for token-critical structured prediction...
ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents
arXiv:2603.00026v1 Announce Type: new Abstract: Effective memory management is essential for large language model (LLM) agents handling long-term interactions. Current memory frameworks typically treat agents as passive "recorders" and retrieve information without understanding its deeper implications. They may fail in...
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
arXiv:2603.02214v1 Announce Type: new Abstract: Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work has explored secure and distributed inference from disparate perspectives, a...
Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents
arXiv:2603.02239v1 Announce Type: new Abstract: The Engineering Reasoning and Instruction (ERI) benchmark is a taxonomy-driven instruction dataset designed to train and evaluate engineering-capable large language models (LLMs) and agents. This dataset spans nine engineering fields (namely: civil, mechanical, electrical, chemical,...
Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach
arXiv:2603.02359v1 Announce Type: new Abstract: Digital advertising increasingly relies on visual content, yet marketers lack rigorous methods for understanding how specific visual attributes causally affect consumer engagement. This paper addresses a fundamental methodological challenge: estimating causal effects when the treatment,...
AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows
arXiv:2603.02601v1 Announce Type: new Abstract: Autonomous AI agents are deployed at unprecedented scale, yet no principled methodology exists for verifying that an agent has not regressed after changes to its prompts, tools, models, or orchestration logic. We present AgentAssay, the...
SorryDB: Can AI Provers Complete Real-World Lean Theorems?
arXiv:2603.02668v1 Announce Type: new Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed of competition problems, hillclimbing the SorryDB benchmark will yield tools...
EvoSkill: Automated Skill Discovery for Multi-Agent Systems
arXiv:2603.02766v1 Announce Type: new Abstract: Coding agents are increasingly used as general-purpose problem solvers, but their flexibility does not by itself confer the domain expertise needed for specialized tasks. Recent work addresses this through \textit{agent skills}: reusable workflows, and code,...
Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification
arXiv:2603.02798v1 Announce Type: new Abstract: As LLM-powered agents have been used for high-stakes decision-making, such as clinical diagnosis, it becomes critical to develop reliable verification of their decisions to facilitate trustworthy deployment. Yet, existing verifiers usually underperform owing to a...
RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization
arXiv:2603.03078v1 Announce Type: new Abstract: Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM agents to tackle complex tasks via multi-step, tool-integrated reasoning. However, an inherent limitation of existing...
Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation
arXiv:2603.03080v1 Announce Type: new Abstract: LLM-based explainable recommenders can produce fluent explanations that are factually correct, yet still justify items using attributes that conflict with a user's historical preferences. Such preference-inconsistent explanations yield logically valid but unconvincing reasoning and are...
Odin: Multi-Signal Graph Intelligence for Autonomous Discovery in Knowledge Graphs
arXiv:2603.03097v1 Announce Type: new Abstract: We present Odin, the first production-deployed graph intelligence engine for autonomous discovery of meaningful patterns in knowledge graphs without prior specification. Unlike retrieval-based systems that answer predefined queries, Odin guides exploration through the COMPASS (Composite...
Universal Conceptual Structure in Neural Translation: Probing NLLB-200's Multilingual Geometry
arXiv:2603.02258v1 Announce Type: new Abstract: Do neural machine translation models learn language-universal conceptual representations, or do they merely cluster languages by surface similarity? We investigate this question by probing the representation geometry of Meta's NLLB-200, a 200-language encoder-decoder Transformer, through...
CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
arXiv:2603.02547v1 Announce Type: new Abstract: We study why continuous diffusion language models (DLMs) have lagged behind discrete diffusion approaches despite their appealing continuous generative dynamics. Under a controlled token--recovery study, we identify token rounding, the final projection from denoised embeddings...
Mozi: Governed Autonomy for Drug Discovery LLM Agents
arXiv:2603.03655v1 Announce Type: new Abstract: Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and poor long-horizon reliability....
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
arXiv:2603.03686v1 Announce Type: new Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language Model (LLM) agents face significant...
Phi-4-reasoning-vision-15B Technical Report
arXiv:2603.03975v1 Announce Type: new Abstract: We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building...
Towards Realistic Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
arXiv:2603.04191v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users share complex and diverse preferences over extended interactions. However, assessing how well LLMs can follow these preferences in realistic, long-term situations remains underexplored....
$\tau$-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
arXiv:2603.04370v1 Announce Type: new Abstract: Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, proprietary, and unstructured corpora during live interactions with users. Yet most existing benchmarks evaluate retrieval...
Discovering mathematical concepts through a multi-agent system
arXiv:2603.04528v1 Announce Type: new Abstract: Mathematical concepts emerge through an interplay of processes, including experimentation, efforts at proof, and counterexamples. In this paper, we present a new multi-agent model for computational mathematical discovery based on this observation. Our system, conceived...
When Agents Persuade: Propaganda Generation and Mitigation in LLMs
arXiv:2603.04636v1 Announce Type: new Abstract: Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one...
Model Medicine: A Clinical Framework for Understanding, Diagnosing, and Treating AI Models
arXiv:2603.04722v1 Announce Type: new Abstract: Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable...
Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery
arXiv:2603.04735v1 Announce Type: new Abstract: This paper demonstrates that artificial intelligence can accelerate mathematical discovery by autonomously solving an open problem in theoretical physics. We present a neuro-symbolic system, combining the Gemini Deep Think large language model with a systematic...
Evaluating the Search Agent in a Parallel World
arXiv:2603.04751v1 Announce Type: new Abstract: Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, evaluating these Search Agents presents formidable challenges. First, constructing high-quality deep search benchmarks is prohibitively expensive,...
LLM-Grounded Explainability for Port Congestion Prediction via Temporal Graph Attention Networks
arXiv:2603.04818v1 Announce Type: new Abstract: Port congestion at major maritime hubs disrupts global supply chains, yet existing prediction systems typically prioritize forecasting accuracy without providing operationally interpretable explanations. This paper proposes AIS-TGNN, an evidence-grounded framework that jointly performs congestion-escalation prediction...
Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models
arXiv:2603.04837v1 Announce Type: new Abstract: We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC) system, applied at inference time to large language...
Retrieval-Augmented Generation with Covariate Time Series
arXiv:2603.04951v1 Announce Type: new Abstract: While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the Pressure Regulating and Shut-Off Valve (PRSOV), a high-stakes industrial...