arXiv AI
Aug 10
TransSLR: A Lightweight Transformer for Sign Language Recognition
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
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 Machine Learning
Aug 10
MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
arXiv AI
Aug 10
FutureBridge: Token Selection Beyond Local Preference in Collaborative Decoding
arXiv:2608. 06819v1 Announce Type: cross Abstract: Token-level collaboration allows a large language model (LLM) to assist a small language model (SLM) when their predictions diverge.
arXiv AI
Aug 10
From Plan to Action: How Well Do Agents Follow the Plan?
arXiv:2604. 12147v3 Announce Type: replace-cross Abstract: Agents are commonly instructed to follow a task-specific plan for guidance.
arXiv AI
Aug 10
SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
arXiv AI
Aug 10
Geometry-Aware Camera Localization for Bronchoscopy
arXiv:2608. 07116v1 Announce Type: cross Abstract: Camera localization in bronchoscopy remains a challenging problem due to stringent accuracy requirements, real-time constraints, and limited training data.
arXiv Machine Learning
Aug 10
Quantization Damage Is Multiplicative, Not Additive
arXiv:2608. 06564v1 Announce Type: new Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt.
arXiv Machine Learning
Aug 10
Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression
arXiv:2608. 07001v1 Announce Type: new Abstract: As large language models (LLMs) process increasingly long contexts, KV cache storage and repeated access have become a major bottleneck.
arXiv AI
Aug 10
ForesightSafety-SAGE:A Fully Automated Scenario Generation and Safety Evaluation Framework for LLM Agents
arXiv:2606. 08531v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly evolving from simple text-based interaction systems into LLM agents that can maintain memory, use tools, access external environments, and execute tasks.
arXiv AI
Aug 10
Deal Me Maybe: The Role of Emotions in Multi-Agent Negotiation
arXiv:2608. 06922v1 Announce Type: new Abstract: Negotiation is a demanding social task for LLM agents, requiring strategic reasoning, persuasion, and interpersonal adaptation.
arXiv AI
Aug 10
Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents
arXiv:2608. 06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions.
arXiv AI
Aug 10
A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers
arXiv:2608. 06694v1 Announce Type: new Abstract: Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is laborious.
arXiv AI
Aug 10
PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents
arXiv:2608. 07438v1 Announce Type: new Abstract: Human-like cognition does not select past experience by topical similarity alone: affective significance and unresolved conflict also shape what becomes accessible.
arXiv Machine Learning
Aug 10
The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought
arXiv:2605. 18079v2 Announce Type: replace Abstract: Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconnect them from the models used in practice.
arXiv AI
Aug 10
Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitation
arXiv:2608. 06955v1 Announce Type: new Abstract: Large language models (LLMs) are trained on corpora that contain expressions of human judgment about films, books, music, and more.
arXiv AI
Aug 10
Stability of Transformers under Layer Normalization
arXiv:2510. 09904v2 Announce Type: replace-cross Abstract: Despite their widespread use, training deep Transformers can be unstable.
arXiv AI
Aug 10
Toward a Causal Data Management Ecosystem for Decision Making and Agentic AI
arXiv:2608. 07214v1 Announce Type: cross Abstract: Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others.
arXiv AI
Aug 10
Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs
arXiv:2608. 07202v1 Announce Type: new Abstract: Systematic reviews of Randomised Controlled Trials (RCTs) are routinely used as evidence for clinical care guidelines.
arXiv AI
Aug 10
WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN
arXiv:2608. 07267v1 Announce Type: new Abstract: Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VLA) policies that map egocentric observations and language instructions directly to navigation actions.
arXiv Machine Learning
Aug 10
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
arXiv:2608. 06559v1 Announce Type: new Abstract: Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable uncertainty estimates, and severe cold starts.