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BlueFin: Benchmarking LLM Agents on Financial Spreadsheets

arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet wo

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Human DGX agent

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BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies

DGX agent

arXiv:2605.30660v1 Announce Type: new Abstract: Test-time scaling for vision-language-action (VLA) policies, methods such as RoboMonkey, SEAL, MG-Select, and V-GPS, samples K candidate action chunks a

model-releasesarxiv-cs-lg

1 Jun 2026

Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies

arXiv:2512.19673v3 Announce Type: replace-cross Abstract: Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms.

Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models

arXiv:2510.11683v3 Announce Type: replace-cross Abstract: A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) is the intractability of their likelihood f

Bounded Behavioral Indistinguishability for Black-Box LLM Distillation

model-releasesarxiv-cs-cl

arXiv:2605.30448v1 Announce Type: cross Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically

Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems

arXiv:2411.13865v4 Announce Type: replace-cross Abstract: Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balan

Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression

arXiv:2602.08885v5 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises t

Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning

arXiv:2605.30652v1 Announce Type: new Abstract: Traditional multi-modal financial forecasting often relies on scalar sentiment scores, which fail to capture the nuances of financial news. To address t

Building Generalization Into Behavior Generation Via Adaptive Compositions of Regularities

arXiv:2605.31110v1 Announce Type: new Abstract: Generalization in robotics requires prior knowledge about how the world is structured, yet this structure changes from one situation to the next. This p

safetyarxiv-cs-ro

Applications

Bundesrecht: An Open Library and Corpus for German Statutory Reference Processing

arXiv:2605.31338v1 Announce Type: new Abstract: Statutory references are central to legal language understanding, but are difficult to process automatically, as they appear in compact and variable sur

applicationsarxiv-cs-cl

CacheProbe: Auditing Prompt Cache Isolation in Gateway APIs

arXiv:2605.30613v1 Announce Type: cross Abstract: Over the past year, prompt caching in Large Language Models (LLMs) has become increasingly more popular across inference APIs. Prompt caching helps sa

Calibrated Preference Learning: The Case of Label Ranking

arXiv:2605.30447v1 Announce Type: cross Abstract: Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively stud

Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture

arXiv:2601.22412v2 Announce Type: replace Abstract: Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation

safetyarxiv-cs-cv

CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping

arXiv:2605.30774v1 Announce Type: new Abstract: Precise camera pose control is critical for video diffusion, yet maintaining geometric consistency remains a challenge. Existing methods that directly i

researcharxiv-cs-cv

Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air

arXiv:2605.31066v1 Announce Type: new Abstract: Recent aerial vision-language-action (VLA) models show promising single-UAV capabilities, such as tracking moving objects and navigating to language-spe

safetyarxiv-cs-ro

Can BEV Perception Gracefully Degrade under Sensor Failures?

arXiv:2605.30983v1 Announce Type: new Abstract: Despite the remarkable success of multi-modal bird's-eye view (BEV) perception in autonomous driving, current systems exhibit a critical vulnerability:

agentsarxiv-cs-cv

Can LLM Teams Play What? Where? When?

model-releasesarxiv-cs-cl

arXiv:2605.30459v1 Announce Type: new Abstract: Large language models (LLMs) remain limited on tasks requiring indirect reasoning, cultural knowledge, and coordinated hypothesis testing. We investigat

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?

model-releasesarxiv-cs-lg

arXiv:2605.30470v1 Announce Type: new Abstract: Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. Ho

CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law

model-releasesarxiv-cs-cl

arXiv:2605.30497v1 Announce Type: new Abstract: RAG-based legal assistants have been growing in popularity, but LLM hallucinations remain a key issue and potentially undermines justice. While benchmar

Local Ai

CaptionFormer: Unified Segmentation, Tracking, and Captioning for Spatio-Temporal Objects

arXiv:2510.14904v3 Announce Type: replace-cross Abstract: Dense Video Object Captioning (DVOC) is the task of jointly detecting, tracking, and captioning object trajectories in a video, requiring the

local-aiarxiv-cs-ai

Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation

arXiv:2510.22067v3 Announce Type: replace Abstract: Vision language models (VLMs) often generate hallucination, i.e., content that cannot be substantiated by either textual or visual inputs. Prior wor

researcharxiv-cs-cv

Caspar: CUDA Accelerator for Symbolic Programming with Adaptive Reordering

model-releasesarxiv-cs-ro

arXiv:2605.30583v1 Announce Type: new Abstract: We present Caspar, a library that makes the power of modern GPUs more accessible in robotics and provides a state-of-the-art nonlinear GPU solver that c

Causal Evaluation of Membership Inference Attacks

arXiv:2602.02819v3 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify mem

safetyarxiv-cs-lg

CellBRIDGE: Learning Cellular Trajectories via Interaction-Aware Alignment

arXiv:2605.30635v1 Announce Type: new Abstract: Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive me

safetyarxiv-cs-lg

Certified Circuits: Stability Guarantees for Mechanistic Circuits

arXiv:2602.22968v3 Announce Type: replace Abstract: Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pur

researcharxiv-cs-ai

Chain-of-Thought and Compressed Looped Transformers: A Memory-Budget Separation

arXiv:2605.30757v1 Announce Type: new Abstract: Chain-of-thought prompting and looped Transformers both give a fixed model more test-time computation, but they differ in what they remember. Chain-of-t

Chain-of-Thought Reasoning In The Wild Is Not Always Faithful

arXiv:2503.08679v5 Announce Type: replace Abstract: Recent studies indicate that when faced with explicit biases in prompts, models often omit mentioning these biases in their Chain-of-Thought (CoT) o

Chatterbox-Flash: Prior-Calibrated Block Diffusion for Streaming Zero-Shot TTS

arXiv:2605.30748v1 Announce Type: cross Abstract: We present Chatterbox-Flash, a zero-shot text-to-speech model obtained by fine-tuning a pretrained autoregressive TTS decoder into a block-diffusion d

researcharxiv-cs-ai

Chem-PerturBridge: a harmonized compendium of small molecule perturbation transcriptomic effects

arXiv:2605.31522v1 Announce Type: new Abstract: Large perturbation models require training data encompassing chemical, cellular, and assay diversity. Current transcriptomic resources for small-molecul

Choosing the Lens: Strategic Perspective Activation in Context-Dependent Argumentation

arXiv:2605.31581v1 Announce Type: new Abstract: The same arguments often need to be evaluated under different external regimes. An agent with influence over the regime has a strategic lever that stand

agentsarxiv-cs-ai

Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception

arXiv:2603.23977v2 Announce Type: replace-cross Abstract: Deep networks often rely on architectural heuristics to shape representation evolution, limiting their ability to model data governed by intri

researcharxiv-cs-ai

Clustering Guided Domain-Specific Pretrained Foundation Model Very High-Resolution Arctic Remote Sensing

arXiv:2605.30467v1 Announce Type: new Abstract: This study introduces a novel Arctic-focused remote sensing foundation model (RSFM) by combining diversity-aware regional-scale image curation with mask

researcharxiv-cs-cv

Local Ai

CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation

arXiv:2605.30668v1 Announce Type: cross Abstract: Dialogue topic segmentation is critical in many human-AI collaborative applications which requires identifying heterogeneous boundary cues, including

local-aiarxiv-cs-ai

CodeGolf Bench: A Multi-Language Benchmark for Evaluating Concise Code Generation Capabilities of Large Language Models

arXiv:2605.30394v1 Announce Type: cross Abstract: This paper introduces Code Bench, a benchmark capable of evaluating Large Language Models (LLMs) concise code generation abilities in 60 programming l

CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference

model-releasesarxiv-cs-cv

arXiv:2605.31591v1 Announce Type: new Abstract: Models for AI-based skin cancer screening suffer a severe performance drop when shifting from expert dermoscopic (source) images to consumer-grade clini

COFT: Counterfactual-Conformal Decoding for Fair Chain-of-Thought Reasoning in Large Language Models

arXiv:2605.30641v1 Announce Type: cross Abstract: Large language models (LLMs) can reveal and amplify societal biases during chain-of-thought (CoT) generation. We present COFT (Chain of Fair Thought),

Applications

Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement

arXiv:2605.30981v1 Announce Type: new Abstract: Autoregressive language models frequently degrade during long-horizon generation, producing repetitive text, losing instruction adherence, and exhibitin

applicationsarxiv-cs-cl

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

arXiv:2605.31264v1 Announce Type: new Abstract: LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and in

agentsarxiv-cs-ai

Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination

arXiv:2605.31058v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as the cornerstone for shaping the remarkable coding abilities of Large Langu

researcharxiv-cs-cl

CoMem: Context Management with A Decoupled Long-Context Model

arXiv:2605.30842v1 Announce Type: new Abstract: Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this p

agentsarxiv-cs-lg

Hardware

CoMo3R-SLAM: Collaborative Monocular Dense SLAM with Learned 3D Reconstruction Priors for Outdoor Multi-Agent Systems

arXiv:2605.30488v1 Announce Type: new Abstract: Collaborative dense SLAM is essential for multi-robot teams to achieve scalable and consistent 3D perception across large-scale outdoor environments. Ex

hardwarearxiv-cs-ro

Comparing LLM-Based Conversational and Graphical Interfaces for Industrial Decision Tasks: An Exploratory Mixed-Methods Study

arXiv:2605.31224v1 Announce Type: cross Abstract: The use of Generative AI Conversational User Interfaces (CUI) as a new way to access and analyze data is growing in all sectors, and the industrial on

agentsarxiv-cs-ai

COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents

arXiv:2605.30838v1 Announce Type: new Abstract: LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmf

Conditional Coverage Diagnostics for Conformal Prediction

arXiv:2512.11779v2 Announce Type: replace-cross Abstract: Evaluating conditional coverage remains one of the most persistent challenges in assessing the reliability of predictive systems. Although con

Configurable Reward Model for Balanced Safety Alignment

arXiv:2605.30487v1 Announce Type: new Abstract: Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge. Existing instruction-tuned

safetyarxiv-cs-cl

Conformal C2ST: Turning weak classifiers into strong two-sample tests

arXiv:2507.17026v2 Announce Type: replace-cross Abstract: The two-sample testing problem, a fundamental task in statistics and machine learning, seeks to determine whether two sets of samples, drawn f

Conformal Reliability: A New Evaluation Metric for Conditional Generation

arXiv:2605.30807v1 Announce Type: new Abstract: Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliabili

ConSensus: Multi-Agent Collaboration for Multimodal Sensing

arXiv:2601.06453v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However,