arXiv AI
Jun 16
PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents
arXiv:2606. 16215v1 Announce Type: cross Abstract: Multi-turn tool-use agents must reason, call tools, and adapt to observations across several interaction turns.
Fine-tuning and adaptation
LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv AI
Jun 16
Fine-Tuning a 7B Advisor on Free-Tier GPUs: An Adapter-Handoff Recipe and a Synthetic-Data Reliability Caution
arXiv:2504. 15610v4 Announce Type: replace Abstract: Fine-tuning a 7B language model for specialized advising is attractive in resource-constrained settings, but multi-epoch runs routinely exceed the wall-clock limits of the free-tier GPUs (Kaggle, Colab) such users rely on.
arXiv AI
Jun 16
Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation
arXiv:2606. 14755v1 Announce Type: cross Abstract: Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity.
arXiv Machine Learning
Jun 16
Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning
arXiv:2606. 15123v1 Announce Type: cross Abstract: We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context.
arXiv AI
Jun 16
ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies
arXiv:2606. 16826v1 Announce Type: cross Abstract: Generalist manipulation policies are increasingly presented as foundation models for robotic control, but their real-world generalization remains difficult to diagnose.
arXiv AI
Jun 16
ACC: Compiling Agent Trajectories for Long-Context Training
arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.
arXiv Machine Learning
Jun 16
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.
arXiv Machine Learning
Jun 16
Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective
arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.
arXiv Machine Learning
Jun 16
ExpRL: Exploratory RL for LLM Mid-Training
arXiv:2606. 17024v1 Announce Type: new Abstract: Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model.
arXiv Machine Learning
Jun 16
PhysGuard: Fisher-Guided Gradient Projection for Sim-to-Real Neural PDE Surrogates
arXiv:2606. 16602v1 Announce Type: new Abstract: Neural operator models trained on simulation data often lose accuracy when applied to experimental measurements due to the sim-to-real gap.
arXiv AI
Jun 16
EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
arXiv:2508. 17742v3 Announce Type: replace-cross Abstract: Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific progress.
arXiv AI
Jun 16
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.
arXiv AI
Jun 16
Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains
arXiv:2604. 02343v2 Announce Type: replace-cross Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute.
arXiv AI
Jun 16
AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
arXiv:2606. 16292v1 Announce Type: cross Abstract: The rapid proliferation of machine learning model reuse has transformed the AI ecosystem into a highly interconnected supply chain.
arXiv AI
Jun 16
Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
arXiv:2606. 15631v1 Announce Type: cross Abstract: Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute.
arXiv AI
Jun 16
FusionRS: A Large-Scale RGB-Infrared Remote Sensing Dataset for Dual-Modal Vision-Language Foundation Models
arXiv:2606. 17020v1 Announce Type: cross Abstract: Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored.
arXiv AI
Jun 16
CogGuard: Cognitive and Operational Profiling for Proactive Warning in Edge Intelligent Services
arXiv:2606. 15199v1 Announce Type: new Abstract: Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints.
arXiv AI
Jun 16
Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection
arXiv:2606. 15117v1 Announce Type: cross Abstract: The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both.
arXiv AI
Jun 16
Intelligence Is Not the Bottleneck: Validating an LLM First-Pass Manuscript Score Against Peer-Review Outcomes
arXiv:2606. 15887v1 Announce Type: cross Abstract: Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns.
arXiv AI
Jun 16
PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
arXiv:2606. 16074v1 Announce Type: cross Abstract: Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research.