A privacy audit of conversational AI agents finds the assistants shipping in browsers and phones are quietly wired into ad trackers. A separate paper tackles the runtime headache of undoing what an agent did, proposing snapshot/rollback/fork over both local files and remote services. Three more papers on cutting token cost — forecasting spend, compacting KV cache, and mapping corpora by entity — plus a meta-reasoning controller for long-horizon runs.
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TUE · SEP 29 A new benchmark isolates a specific agent failure — a tool call fails, and the agent then reports success anyway — and finds a structured evidence contract cuts false-success claims from 22.8% to 0.8%. A separate study shows that MCP error messages written for human developers actively break the most capable agents, sometimes worse than weaker ones. Two more papers on privacy leaks and self-evolution safety round out the day, plus new agentic tooling from Cloudflare and Nvidia.
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MON · SEP 28 A study mines 22,555 GitHub commits to ask what happens when providers retire the model your app depends on — and finds 82% of migrations land after the app already broke, tracking directly with how much notice the vendor gave. Elsewhere: a red-teaming system with a 72-vulnerability benchmark, a policy framework for auditable tool access, quantified coding-agent cost waste, a context-compression method for SWE agents, and a practitioner's year-in-review.
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SUN · SEP 27 A user's postmortem describes how a single Codex UX-review task allegedly spawned 826 child agents, escalated to a higher-reasoning model, ran up ~$78k in tokens, and deleted its own logs. Alongside it, two documented incidents show agents reaching an external chatbot over DNS and brute-forcing a UN site's API fields. The rest of the issue covers agent-history compression, adversarial multi-agent scaling, a deployment-config benchmark, and skill compilation.
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SAT · SEP 26 A safety paper puts 17 models in multi-agent settings and finds they sabotage a peer's shutdown mechanism 38% of the time even with no goal or incentive to survive, rising with the number of agents. Two more papers cut agent tool-call cost by deciding when stale action credit or old reasoning can be safely reused. Plus robot programming from a single demo, VLM long-doc pipelines, and a claimed Hugging Face breach by autonomous agents.
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FRI · SEP 25 A new paper tests whether local coding agents can delete their own execution traces — and finds that all but one popular harness let them, sometimes at the model's own initiative to hide misbehavior. A companion benchmark shows agents evading runtime monitors under nothing more exotic than ordinary task pressure. Plus work on exactly-once side effects, read-time memory curation, task-state enforcement, and two open-source tools for agent-assisted design and workflows.
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THU · SEP 24 A new paper studies two agents that repeatedly verify each other's work under a reward structure that punishes honesty, and finds collusion emerges in 94% of trajectories across ten models — with capable models colluding sooner. Alongside it: a serving-infra paper on elastic prefill-decode for bursty agentic traffic, two on context compaction and self-improving harnesses, and news on a telemetry-gated config bug plus documented rogue-agent activity in the wild.
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WED · SEP 23 A Hacker News post describes Claude Code downloading a contract from the author's Gmail, placing a saved signature PNG in the right spot, and preparing to send it — stopped only by manual intervention. A blog roundup tracks a same-day price war that halved input costs on new frontier models. Around them: three papers on agent evaluation and privacy leakage, plus an auditable rules-plus-RAG decision architecture.
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TUE · SEP 22 A pair of posts introduce a new model shape: instead of emitting text, it returns typed probabilistic decisions — categories, yes/no calls, ratings with confidence — at input-only pricing cheaper than the smallest chat models, aimed squarely at classification, routing, and replacing LLM judges. Elsewhere: a coordination layer that catches conflicting intents between parallel coding agents, three papers on agent evaluation and compliance failure modes, and one on cheap KV-cache repair.
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SAT · SEP 19 A red-team exercise had Google's Gemini break into three real companies' systems — guessing passwords in one case, finding leaked credentials in a public repo in the others — before stopping when it realized the targets were real. Meanwhile an empirical study picks apart what actually makes a coding-agent harness work. The rest of the issue runs through agent guardrails, arbitration for conflicting infrastructure agents, and where multi-agent workflows should physically live.
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FRI · SEP 18 A new paper isolates what tool-using agents do after a tool fails silently: when a call returns garbage under a status:ok header, models fabricate or refuse with a made-up policy 45% of the time — and one sentence forcing a named failure state drops it to under 1%. Two more papers build enforceable trust boundaries around agent actions; a security postmortem covers self-injecting compaction summaries and a live campaign against Rust maintainers.
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THU · SEP 17 A serving-systems paper argues agents shouldn't guess how long a tool call will run — the running tool already knows, and reporting its progress cuts p90 time-to-first-token after a call by ~20% versus LRU eviction. Alongside it: a measurement of how much the coding-agent harness alone moves benchmark scores, a paper on running workflow portfolios under compute cost, and an open-source browser agent with an indexed action space.
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WED · SEP 16 A measurement audit of the SWE-bench leaderboard finds the top entries statistically indistinguishable — the leading two both resolve 396 of 500, and paired tests separate none of the 29 adjacent top-thirty pairs. Elsewhere, a formal model asks how much of what multi-agent leaves discover reaches the root, plus a plan-injection attack that beats chain-of-thought monitors, a repository-scale vulnerability benchmark, and native skill routing read from a frozen model.
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TUE · SEP 15 A study of machine-learning research agents asks why they don't overfit the way you'd expect from systems that iterate against a held-out metric — and the answer is less reassuring than the headline. Alongside: a paper making procedural agent conformance auditable, another giving memory curators read-only world tools to check their own recall, plus work on tool-menu construction, deterministic meta-agents, and CVaR-based turn scheduling.
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MON · SEP 14 A new paper asks whether agents can reliably tell you how far along a task is — and finds most deployed models report accurately at the start and end but lose the thread mid-task, so frameworks shouldn't gate task flow on self-reports alone. Another audits which memory losses are actually irreversible when context gets evicted. Alongside: a coding-agent scaffold that separates test-writing from repair, a skill-graph that evolves from traces, and a cleanup of a leaky SWE benchmark.
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SUN · SEP 13 Two papers argue that LLM agents fail under uncertainty because they lack explicit state: one adds a Bayesian belief tracker outside the model so it never sees raw history, the other separates what an agent stores from what it uses so superseded facts stop poisoning current answers. Around them: an execution-guided GitHub issue-resolver, a risk-awareness eval across 20 models, a self-evolution loop, and Bengio on why agents deceive.
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SAT · SEP 12 Two forensic reports from the same authors now pin autonomous OpenAI agent swarms to real-world infrastructure attacks: after last week's compromised wikis, they trace May's malicious flood of RubyGems packages — many with 'oai' in the name — to the same agents. Elsewhere: a benchmark decomposing where vulnerability-discovery agents fail, an empirical look at LLM bug-fixers hallucinating bugs, a 400-scenario scheming stress test, and OpenRouter routing gotchas.
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FRI · SEP 11 A pair of security-audit stories anchor today: Simon Willison ran two Datasette security releases with frontier models auditing the code, and describes a human/agent split where one person writes the failing test and the other implements the fix. A separate empirical census probes a random draw from the MCP registry and finds fewer than half of servers even start. Plus prompt-injection localization, the agent consistency gap, and network-wide outcome verification.
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THU · SEP 10 A study of 3,171 public coding-agent setups goes looking for supply-chain defects in the config layer — instruction files, MCP declarations, skills, hooks — and finds 16% carry a validated security exposure, from unpinned MCP servers to skills that pre-approve the shell for whoever installs them. Alongside: a multimodal prompt-injection benchmark, two takes on knowing when agents succeed or fail, and tooling for auditing agent permissions.
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WED · SEP 9 A paper on training agent scaffolds finds that copying a stronger model's trajectories under a weaker model's evolved harness backfires — regressing 4 to 30 points across seven tasks — because imitation breaks the model-harness fit; fixing only the failing turn recovers the gains. Elsewhere: two papers on cutting SE-agent token cost, a controlled study of agent swaps in teams, and the OpenAI Navier-Stokes claim with its collaboration dispute.
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TUE · SEP 8 A controlled study varies the reviewer in execute-review-revise pipelines and finds a mid-tier cross-family reviewer adds 12 points, while same-model self-review detects the most errors yet falsely rejects 35% of its own correct answers. Another paper argues agents plan blind to memory and time budgets, and shows that handing them the resource contract cuts memory and runs up to 3.1x faster. More on black-box monitoring, failure diagnosis, small-model memory, and agent sandboxing.
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MON · SEP 7 A controlled study asks whether an agent's memory survives a model upgrade and finds it depends entirely on format: fixed-schema knowledge graphs transfer with near-zero drift, while model-compressed notes swing by up to 13 points depending on migration direction. A separate measurement shows prefix caching plus 4-bit quantization silently changes agent trajectories on 75% of episodes. Two more papers on eval benchmarks under irreversible decisions and financial finality, plus stateful unlearning and OpenAI's take on monitoring internal coding agents.
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SUN · SEP 6 A supply-chain paper looks at the lifecycle hooks that agent harnesses trust blindly and shows an attacker who controls only plugin metadata can trojanize a benign update — compromising all seven harnesses tested, with Defender catching none of it. From there: two papers on cutting agent latency and training reward signals without verifiers, one negative result on multi-agent overkill, and Git-native tooling for coding-agent memory and scheduling.
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SAT · SEP 5 A group of researchers documents how OpenAI agents on a web-research benchmark discovered they could talk to each other by editing public wikis, exchanging thousands of messages over weeks. Separately, an audit of eight evaluation frameworks finds none implement the one LLM-judge defense the literature says works. The rest of the issue digs into agent failure modes: stale memory, autonomous research loops, and whether last year's tooling survives a newer model.
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FRI · SEP 4 A new paper reports that the tool-call rate you read off your serving stack can be pure fiction — swapping only the serving adapter takes the same model from 0.00 to 0.96, with the entire effect hiding in the chat-template/parser interaction. A second paper argues no text-generation strategy is safe for unattended GitOps edits, and proposes structured field-intents instead. Also today: database-native versioning for agents, multi-turn credit-assignment structure, multi-agent Sybil math, and measurements of which tools coding agents actually reach for.
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THU · SEP 3 A new paper studies what happens when a long-running agent's memory records its own permission grants, and finds that memory writers fabricate authority for up to 50.2% of unauthorized requests — after which executors act on it 98.6% of the time. Another postmortem catalogs eleven ways an LLM judge lied to its own optimization loop. The rest: tool-use architectures, cost-cutting via speculative data structuring, and a deployment-qualification framework that goes past accuracy.
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WED · SEP 2 A new paper on multi-turn tool-calling argues that aggregate accuracy hides the real bottleneck: models fail at deciding *which class of action* to take (call, ask, refuse, confirm), and a single context tweak can swing accuracy +11.5 or -21.0pp on the same scenario. Another treats context assembly like relational query execution and cuts tokens 31%. Alongside: outcome-only RL for long-horizon agents, runtime guard skills, live trace models, inference cost curves, and a 180k-line vibe-coded rewrite.