Make agents use tools and reusable skills more reliably, Identify and reduce safety, jailbreak, and alignment risks
Today tracks: make agents use tools and reusable skills more reliably, make agents use tools and reusable skills more reliably, identify and reduce safety, jailbreak, and alignment risks.
This issue fetched and deduplicated 273 candidate papers from the 2026-06-01 source date, then selected 5 featured papers and 15 additional mentions.
Featured
- 1Cosmos 3: Omnimodal World Models for Physical AI🔗
- 2Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models🔗
- 3Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation🔗
- 4KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators🔗
- 5EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement🔗
What is worth tracking today
Today’s high-signal papers point to: make agents use tools and reusable skills more reliably, make agents use tools and reusable skills more reliably, identify and reduce safety, jailbreak, and alignment risks. The notes below focus on the core problem, method signal, main claim, and keywords.
Featured papers: title, takeaway, and verification trail
make agents use tools and reusable skills more reliably
Make agents use tools and reusable skills more reliably. Core signal: We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture. Code/data availability and transfer limits should be confirmed in the original paper.
make agents use tools and reusable skills more reliably
Make agents use tools and reusable skills more reliably. Core signal: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined. Code/data availability and transfer limits should be confirmed in the original paper.
identify and reduce safety, jailbreak, and alignment risks
Identify and reduce safety, jailbreak, and alignment risks. Core signal: Digital platforms increasingly operate as isolated information silos, limiting their ability to construct comprehensive user representations across domains. Code/data availability and transfer limits should be confirmed in the original paper.
make agents use tools and reusable skills more reliably
Make agents use tools and reusable skills more reliably. Core signal: Production inference increasingly targets a heterogeneous mix of accelerators. Code/data availability and transfer limits should be confirmed in the original paper.
improve code generation, execution feedback, and automated repair
Improve code generation, execution feedback, and automated repair. Core signal: Audio tokenizers serve as the discrete interface between continuous audio and Audio Language Models (ALMs), but existing tokenizers often struggle to support both understanding and generation. Code/data availability and transfer limits should be confirmed in the original paper.
Other papers worth tracking
Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models: Tracks task design, metrics, and failure cases; useful for model evaluation and regression testing.
GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception: Tracks retrieval, knowledge-base QA, and evidence reliability; useful for RAG evaluation and enterprise knowledge systems.
Tiny Collaborative Inference for Occlusion-Robust Object Detection: Tracks retrieval, knowledge-base QA, and evidence reliability; useful for RAG evaluation and enterprise knowledge systems.
Do Transformers Need Three Projections? Systematic Study of QKV Variants: Tracks inference cost, latency, throughput, and deployment constraints; useful for systems optimization.
Pathway-Structured Privileged Distillation for Deployable Computational Pathology: Tracks retrieval, knowledge-base QA, and evidence reliability; useful for RAG evaluation and enterprise knowledge systems.
RRISE: Robust Radius Inference via a Surrogate Estimator: Tracks task design, metrics, and failure cases; useful for model evaluation and regression testing.
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing: Tracks retrieval, knowledge-base QA, and evidence reliability; useful for RAG evaluation and enterprise knowledge systems.
Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
Acceptance-Test-Driven Evaluation Protocols for Business-Centric LLM Systems: Tracks tool use, execution feedback, and reusable capabilities; useful for agent workflow reliability.
Reading boundaries
- Automated ranking favors papers with community, code, and applied-engineering signals.
- Briefs are based on titles, abstracts, and public metadata by default, not full-paper review.
- External API failures degrade optional signals and are reflected in internal records.