arXiv AI
Jul 1
Can Physician Expertise Improve Machine Learning Identification of Delirium?
arXiv:2606. 30651v1 Announce Type: cross Abstract: Delirium is common in hospitalized patients and is often missed in routine care.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jul 1
Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
arXiv:2606. 30923v1 Announce Type: cross Abstract: Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model training.
arXiv AI
Jul 1
Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation
arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.
arXiv AI
Jul 1
OpenLife: Toward Open-World Artificial Life with Autonomous LLM Agents
arXiv:2606. 31046v1 Announce Type: new Abstract: Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds.
arXiv AI
Jul 1
PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
arXiv:2606. 31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions.
arXiv Machine Learning
Jul 1
TORA: Topological Representation Alignment for 3D Shape Assembly
arXiv:2604. 04050v2 Announce Type: replace-cross Abstract: Flow-matching methods for 3D shape assembly learn point-wise velocity fields that transport parts toward assembled configurations, yet they receive no explicit guidance about which cross-part interactions should drive the motion.
arXiv Machine Learning
Jul 1
T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World
arXiv:2603. 18481v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions.
arXiv AI
Jul 1
Loc2Repair: A Framework for Evaluating the Impact of File-Level Issue Localization in Repo-Level LLM Repair
arXiv:2606. 30963v1 Announce Type: cross Abstract: Repository-grounded automated repair is often reported as a single end-to-end capability, which hides distinct failure modes such as poor file targeting, incorrect patch synthesis, and failed iterative debugging.
arXiv Machine Learning
Jul 1
Low-dimensional topology of deep neural networks
arXiv:2606. 31856v1 Announce Type: new Abstract: We study layered models, including feedforward networks, ResNets, and transformers, by limiting each layer to a width of $d = 3$, i.
arXiv Machine Learning
Jul 1
Revocable Learned State via Process Sidecars
arXiv:2606. 30788v1 Announce Type: new Abstract: Language models are often adapted in stages: a public skill phase, a private memory phase, and a later safety phase that learns to refuse outputs tied to the remembered entities.
arXiv AI
Jul 1
PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
arXiv:2606. 32004v1 Announce Type: new Abstract: Policy-grounded document review requires determining whether a target document complies with organization-specific policies, guidelines, or playbooks.
arXiv AI
Jul 1
Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection
arXiv:2606. 30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging.
arXiv AI
Jul 1
RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents
arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.
arXiv AI
Jul 1
Are Video Reasoning Models Ready to Go Outside?
arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.
arXiv AI
Jul 1
CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation
arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.
arXiv AI
Jul 1
Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support
arXiv:2606. 30887v1 Announce Type: cross Abstract: Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive metric.
arXiv AI
Jul 1
Can LLMs Imagine Moral Alternatives Beyond Binary Dilemmas?
arXiv:2606. 31213v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as moral advisors and agents, they need to address dilemmas between two competing values.
arXiv AI
Jul 1
Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts
arXiv:2508. 01273v3 Announce Type: replace Abstract: Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language Models (LLMs) as they integrate increasingly diverse data sources.
arXiv AI
Jul 1
An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.
arXiv AI
Jul 1
SMART: When is it Actually Worth Expanding a Speculative Tree?
arXiv:2604. 09731v2 Announce Type: replace-cross Abstract: Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single target-model forward pass.