3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
We’d like to hear from people who have been monitored or assessed by AI at work and what they thought about the experience Firms are increasingly using AI to monitor or assess their employees, leading to concerns about w…
By Thomas Kurian • 29-minute read
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Full-stack safety solution for physical AI is being used by robotics companies.
Rein Security series_a reported $25m
A 16-year-old planned his hiking route on Crown Mountain near Vancouver using Anthropic's Claude and had to be airlifted off a steep rock face.
According to CrowdStrike, a suspected Chinese-speaking attacker hacked multiple South Korean financial institutions.
We’re launching the Anthropic Cyber Mission, a long-term commitment to securing the systems everyone depends on.
After extensive testing, Common Sense Media found the chatbot failed in five key areas.
SPONSORED FEATURE: Making the link between business strategy and how the workforce adopts technology will be crucial to business success in the agentic AI era
yay small models
European AI Innovation Month Anonymous (not verified) Thu, 10/08/2026 - 09:00 14 October 2026 - 17 November 2026 From 14 October to 17 November 2026, this initiative led by the European Commission in partnership w…
Finches reported €2m
On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models.
A summary of a Chinese essay, with a few short translated excerpts.
Reconstructing complete, scene-aligned 3D objects from casual images requires integrating sparse, uncertain observations and inferring surfaces hidden by occlusions.
Giant artificial intelligence rounds have pushed U.S.
arXiv:2610.08959v2 Announce Type: replace-cross Abstract: On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-learning rewar…
arXiv:2610.09763v2 Announce Type: replace-cross Abstract: Offline reinforcement learning enables reward-driven policy improvement from fixed datasets without requiring online exploration, making it particularly attractiv…
arXiv:2610.10226v2 Announce Type: replace-cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete?
arXiv:2610.08875v2 Announce Type: replace Abstract: Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance.
arXiv:2610.08900v2 Announce Type: replace Abstract: Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done.
arXiv:2610.08901v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have attracted growing attention for improving reasoning through interaction among multiple agents.
arXiv:2610.08923v1 Announce Type: new Abstract: Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints.
arXiv:2610.08927v1 Announce Type: new Abstract: Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discov…
arXiv:2610.08967v1 Announce Type: new Abstract: Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts.
arXiv:2610.08993v1 Announce Type: new Abstract: As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML).
arXiv:2610.09000v1 Announce Type: new Abstract: As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of loca…
arXiv:2610.09002v2 Announce Type: replace Abstract: When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward.
arXiv:2610.09007v1 Announce Type: new Abstract: Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules.
arXiv:2610.09016v1 Announce Type: new Abstract: Vision-language-action (VLA) models map visual observations and language instructions to continuous robot actions.
arXiv:2610.09021v1 Announce Type: new Abstract: An agentic system issues several structurally different kinds of LLM calls.
arXiv:2610.09034v1 Announce Type: new Abstract: Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency.
arXiv:2610.09037v1 Announce Type: new Abstract: Supervisory governors can interfere with the tool-using agents they regulate.
arXiv:2610.09053v1 Announce Type: new Abstract: The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of so…
arXiv:2610.09088v1 Announce Type: new Abstract: Agent checkpoint systems decide what state is recovery-relevant, how to snapshot it, and whether rollback is admissible.
arXiv:2610.09112v1 Announce Type: new Abstract: Before tool-using LLM agents are deployed in environmental and geospatial workflows, teams need evidence that an agent reliably selects the right operations against real AP…
arXiv:2610.09115v1 Announce Type: new Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism.
arXiv:2610.09139v1 Announce Type: new Abstract: Language models are more and more often asked for structured output: JSON that follows a schema, or a tool call with typed arguments.
arXiv:2610.09146v1 Announce Type: new Abstract: Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve.
arXiv:2610.09159v1 Announce Type: new Abstract: As autonomous coding agents get increasingly deployed, the risk that accidental or adversarially injected misspecifications in tasks lead to dangerous agent behavior is cri…
arXiv:2610.09164v1 Announce Type: new Abstract: Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed…
arXiv:2610.09193v1 Announce Type: new Abstract: MemoryAgentBench's Conflict Resolution split is read as measuring "selective forgetting".
arXiv:2610.09237v1 Announce Type: new Abstract: Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models.
arXiv:2610.09294v1 Announce Type: new Abstract: Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments.