Search: “model-releases”

Stability Analysis of Sharpness-Aware Minimization

arXiv:2301.06308v2 Announce Type: replace-cross Abstract: Sharpness-aware minimization (SAM) is a training method that seeks to find flat minima in deep learning, resulting in state-of-the-art perform

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Stable Velocity: A Variance Perspective on Flow Matching

DGX agent

arXiv:2602.05435v2 Announce Type: replace Abstract: While flow matching is elegant, its reliance on single-sample conditional velocities leads to high-variance training targets that destabilize optimi

model-releasesarxiv-cs-cv

2 Jun 2026

StemBind: When MLLMs Get Lost Between Rules and Instances in Abstract Visual Reasoning

arXiv:2606.00148v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often know the rule but pick the wrong answer: on abstract visual reasoning (AVR) tasks, a model can describe

Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing

model-releasesarxiv-cs-ro

arXiv:2602.06925v2 Announce Type: replace Abstract: Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requir

StreamingVLM: Real-Time Understanding for Infinite Video Streams

arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i

Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction

arXiv:2601.20803v2 Announce Type: replace Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extrac

Subliminal Learning is a LoRA Artifact

arXiv:2606.00831v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where language models can transmit behavioral traits to other models through seemingly innocuous data (Cloud et al.,

SuperMemory-VQA: An Egocentric Visual Question-Answering Benchmark for Long-Horizon Memory

arXiv:2606.00825v1 Announce Type: new Abstract: AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond

Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection

arXiv:2606.01843v1 Announce Type: cross Abstract: Deepfake detection suffers from poor generalization across forgery methods, as existing models tend to rely on spurious method-specific shortcuts that

Symbolic Neural Generation with Applications to Lead Discovery in Drug Design

arXiv:2510.23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to cons

T1: Tool-integrated Verification for Test-time Compute Scaling in Small Language Models

arXiv:2504.04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs). However,

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

arXiv:2606.02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remain

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe

Task diversity produces systematic transfer but inhibits continual reinforcement learning

arXiv:2606.00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions

TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation

arXiv:2606.00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal

TECCI: Tricky Edits of Collected and Curated Images

arXiv:2606.01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction follow

Temporally-Aligned Evaluation for Audio-Driven Talking Head Generation

arXiv:2606.01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temp

TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge

arXiv:2605.24470v2 Announce Type: replace Abstract: Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an im

TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images

arXiv:2606.01050v1 Announce Type: new Abstract: Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated scree

The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition

arXiv:2606.00545v1 Announce Type: new Abstract: Post-trained language models can recognize their own outputs from a sentence or two out of context. In a companion paper itep{jack2026twomodes} we showe

The Case for Model Science: Verify, Explore, Steer, Refine

arXiv:2606.01189v1 Announce Type: new Abstract: We argue that the AI community is now ready to move beyond benchmarking and consolidate scattered efforts in model analysis into a systematic discipline

The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing

arXiv:2606.02184v1 Announce Type: cross Abstract: These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academ

The Image Reconstruction Game: Drawing Common Ground Through Iterative Multimodal Dialogue

arXiv:2606.01901v1 Announce Type: cross Abstract: We introduce the Image Reconstruction Game, a fully automated benchmark in which a vision-language model issues corrective instructions to an image ge

The Invisible Coalition Partner: How LLMs Vote When Democracy Gets Concrete

arXiv:2606.00048v1 Announce Type: cross Abstract: Prior research has established that instruction-tuned large language models exhibit left-of-center political bias, measured exclusively through abstra

The Refusal--Compliance Tradeoff: A Large-Scale Safety Behavior Audit of Large Language Models

arXiv:2605.05427v2 Announce Type: replace Abstract: Refusal rates are a poor proxy for LLM safety, i.e., a model may over-refuse benign prompts while still complying with harmful ones. We audit both f

The Shape of Wisdom: Decision Trajectories in Language Models

arXiv:2606.01202v1 Announce Type: new Abstract: Language models do not simply choose an answer at the output layer. In a 9,000-trajectory MMLU study across Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct,

Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs

arXiv:2606.00592v1 Announce Type: new Abstract: Effective visual communication stems from the harmony of multiple design principles, such as readability, contrast, alignment, overlap, and coherence, w

Time-Optimal Collision Avoidance Via a Greedy Polynomial Backward Sweep

model-releasesarxiv-cs-ro

arXiv:2606.01169v1 Announce Type: cross Abstract: Spacecraft collision avoidance for low-thrust satellites often requires determining not only how to maneuver, but also how late a maneuver can begin w

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606.01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural la

Tiny Recursive Models for Solving the J2-Perturbed Lambert Problem

arXiv:2606.00895v1 Announce Type: cross Abstract: This paper presents a fast, recursive neural solver for the J2-perturbed Lambert problem based on Tiny Recursive Models (TRM), termed the TRM-Perturbe

TLG: Temporal-Logic Grounding for Video Question Answering via Source-Annotation Reconstruction and Category-Targeted Reasoning

arXiv:2606.01591v1 Announce Type: new Abstract: The TimeLogic Challenge evaluates formal temporal-logic reasoning over video - 16 operators (before, after, until, since, always, co-occur, ordering, ..

Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners

arXiv:2606.01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. Thi

Topological Ignorability for Structural Causal Effects Beyond Means

arXiv:2606.01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

arXiv:2507.09766v2 Announce Type: replace-cross Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM)

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

arXiv:2509.16635v2 Announce Type: replace Abstract: In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, r

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models

arXiv:2606.00919v1 Announce Type: new Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - re

Towards Multidisciplinary Summarization of Hospital Stays: Efficient Sentence-Level Clinical Provenance Categorization

arXiv:2606.02487v1 Announce Type: new Abstract: Effective 'all-team' summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse d

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods

arXiv:2412.19444v2 Announce Type: replace Abstract: Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate

TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents

arXiv:2606.01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existi

Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

arXiv:2304.11127v5 Announce Type: replace-cross Abstract: Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structur

TrustLDM: Benchmarking Trustworthiness in Language Diffusion Models

arXiv:2606.00023v1 Announce Type: cross Abstract: The rapid development of Language Diffusion Models (LDMs) challenges the dominant position of auto-regressive competitors in language processing. Howe

Truth, Trust, and Trouble: Medical AI on the Edge

arXiv:2507.02983v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. Howeve

Truthful AI Advisors: A Pre-Specified Benchmark for Large Language Model Honesty Under Preference Misalignment

arXiv:2606.01456v1 Announce Type: cross Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sale

TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages

arXiv:2606.01322v1 Announce Type: cross Abstract: Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones, c

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

arXiv:2606.01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limi

TVIR: Building Deep Research Agents Towards Text--Visual Interleaved Report Generation

arXiv:2606.02320v1 Announce Type: new Abstract: Deep Research Agents have shown strong capability in multi-step information retrieval, reasoning, and long-form report generation, but existing benchmar

Uncovering Competency Gaps in Large Language Models and Their Benchmarks

arXiv:2512.20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks. These benchmarks provide useful aggregated metrics, but can

Understanding Identity Continuity in Thermal Video through Scene-Level Consistency

arXiv:2606.01694v1 Announce Type: cross Abstract: Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation. We