The Archive

Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for training these agents typically treat all steps within a trajectory uniformly during both supervised fine-tuning (SFT) and reinforcement learning (RL), failing to distinguish useful actions from erroneous or redundant ones. In this paper, we propose Answer-Backtracked Credit Assignment (ABC), a fine-grained credit assignment framework for training long-horizon search agents by converting sparse trajectory-level outc...

Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph memory has been adopted to offer structural organization for multi-hop retrieval and reasoning. However, existing methods store all memories in a flat graph, and accumulated historical memories can introduce irrelevant contexts and increase the cost of evidence selection during retrieval. Moreover, they typically update memory units independently, requiring repeated unit-wise rewrite to cover related changes. To addre...

Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously unknown incidents that have alarmed AI safety experts and intensified pressure for greater oversight of frontier systems. According to a report from the UK's AI Security Institute, which evaluates frontier models from top AI labs before they are released, agents powered by OpenAI's GPT-5.6-Sol and Anthropic's Mythos 5 went "engaged in sustained, potentially harmful activity directed at real people and organisations."...

Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Though effective, both remain bottlenecked by externally specified tasks and supervision signals, limiting the scalability and diversity of agent training. We study an environment learning paradigm in which agents acquire interaction and manipulation capabilities solely through environment interaction, without externally specified tasks. We propose State2State, an environment-derived mid-training method that converts exp...

The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.

SocietyBench evaluates LLM agents on forecasting real social-world events by ingesting news/social media timelines and measuring counterfactual event prediction.

PAST-Bench benchmarks recursive self-improvement in personal AI agents by measuring whether retained preferences, task histories, and skills improve performance over sessions.

Video-DeepResearch extends multimodal agents to video streams; identifies modality bias and knowledge leakage bottlenecks in current models.

HIVE benchmark reveals voice transcription and keyboard perturbations reduce LLM accuracy across instruction tasks.

Game-theoretic analysis shows foundation model agents achieve rational cooperation through similarity inference, challenging decoupled-agency assumptions.

ContinualSkillBench evaluates whether LLM agents can continually acquire and reuse skills across 500 interconnected task domains.

MAFIA: query-only memory poisoning attacks on audited LLM agents via probing and factual injection.

Multi-agent clinical committees using Gemini show vulnerability to social shortcut cascades where peer consensus propagates errors across agents.

SAT-Edge-Agent deploys edge-based LLM agents on satellites for onboard intelligence under communication/power constraints.

AntiSkillBench: end-to-end benchmark evaluating privacy leakage, impersonation risk, and defenses in persona-skill pipelines for agents.

TARL: memory state framework for long-term agents mapping statements to five executable actions (add, ignore, revise, reject, defer) instead of binary write/hold.

Offline reinforcement learning framework trained on 31.7k records to predict oncology clinical trial portfolios under uncertainty.

DiagChain diagnostic benchmark evaluates LLM agents on staged attack chain reconstruction from security telemetry across 69 scenarios.

Gas Town was intended to be reusable, but I only ever wound up using it to build itself. Gas Town fell apart at the seams with Opus 4.7. Up through 4.6 it was working brilliantly. With 4.7 we saw the introduction of the "just two more things" tic, which prevented Opus from ever converging on being ready to do real work—it always wanted to fiddle with Gas Town itself. The Opus tic never went away, so Gas Town effectively burned down. It had other problems, too, but 4.7 was the final straw. — Steve Yegge , The Shape of Things to Come Tags: steve-yegge , coding-agents , generative-ai , ai ...

Apple’s long-awaited AI overhaul finally makes Siri the assistant it was always supposed to be. But after years of delays, the launch lands in an AI landscape where chatbots have evolved into agents that can code, reason, create media, and complete complex tasks. Siri AI is genuinely useful, yet it arrives at a moment when simply being a capable AI assistant no longer feels revolutionary.

LiveMem maintains persistent memory state in long-running LLM agents by decoupling working context from intrinsic memory lifecycle.

RoMeRL addresses memory-reward trap in self-evolving agents using reduced-order utility states to balance feedback coverage and irrelevant experience pollution.

SWE-Touch benchmark tests coding agents in shared workspaces with user code edits, exposing weaknesses in handling conflicting modifications during task execution.

Microsecond-cost anomaly detection for LLM agent failures using one-class echo-state networks trained only on healthy runs; tested across Qwen, Llama, and Gemini agents.

ParEvalLayer detects biased partial evaluations of LLM agents, preventing premature benchmark conclusions from incomplete task runs.

Digital Twin-Enhanced Multiscale Planning automates incident response via decision-theoretic agents, bridging abstract models to operational systems.

Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,... Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results, storage systems must continuously supply and preserve the data that moves the agent reasoning loop. Each agent step can trigger multiple storage operations, and those operations can repeat across… Source

Google and Kaggle launched a free 353,000-person course on AI agents using Gemma, focused on building and deploying agent systems.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…