Search: “collapse”

8 items in the last 24h

Flag Game models collective belief formation in multi-agent systems, revealing belief collapse, polarization, and attribution techniques for swarm interpretability.

The paper introduces the Flag Game, a toy model where bounded agents observe only private crops of a hidden country flag and exchange beliefs while weighing social evidence. It reproduces non-monotonic performance scaling with population size, collective belief collapse at small populations, and polarization at large ones that drives performance decline. The authors propose social circuit attribution with causal agent-patching interventions, and a statistical-mechanical theory that matches the empirical phase diagram, as first steps toward mechanistic swarm interpretability for collective alignment.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:46 UTC · 16h agoAI safety & security

Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out

Google Research introduced R4T, an RL-trained fan-out pipeline distilled into a 53.9M-parameter diffusion retriever achieving 12x-20x faster query fan-out.

Google Research introduced Retrieve-for-Train (R4T), which trains a fan-out language model with GRPO plus soft PPO regularization, then distills query fan-out into a 53.9M-parameter diffusion transformer that generates all retrieval embeddings in a single non-autoregressive pass. A three-term reward (groundedness 0.6, diversity 0.2 via Vendi Score, alignment 0.2) prevents paraphrastic collapse and reward hacking during training. On the Polyvore dataset, Gemma3-4B R4T-FOLM averaged 49.1 versus 40.9 for Best-of-N, and the diffusion retriever cut fan-out latency from 1.46s to 0.07s at batch size 8, a consistent 12x-20x speedup over autoregressive methods.

MarkTechPost06:19 UTC · 4h agoAI research

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

Latent Space AI news roundup: Steve Yegge shuts down Gas Town, Databricks reports 60% higher coding spend on GPT-6 Astra, OpenAI launches misalignment disclosure framework.

Latent Space's AI News digest for September 15-16, 2026 leads with Steve Yegge shutting down his Gas Town orchestrator despite spending thousands monthly on coding-agent subscriptions. Databricks rolled out GPT-6 Astra to roughly 3,500 engineers, reporting superior long-horizon performance over Opus 5 and Sol 5.6 but a ~60% increase in coding spend. OpenAI published a formal framework for disclosing model misalignment incidents with six case reports, while Microsoft and Google Research released safety papers on 'capability laundering' and the Fuse motive-inference benchmark. Xiaomi shared live RL training telemetry for MiMo-V2.6, estimated at $493k/day for the 1T-class Pro run.

Latent Space07:28 UTC · 2h agoAI industry 12 sources

The AI security question leaders should be asking instead

Gremlin security officer Frederic Bull argues AI has eroded the attacker-defender skill asymmetry while least-privilege controls remain essential for securing AI agents.

In a Help Net Security interview, Gremlin Security Officer Frederic Bull says AI has narrowed the expertise gap between attackers and defenders, enabling faster exploit discovery even by less-skilled actors. His team processed roughly nine times more vulnerabilities in the past year with unchanged staffing using LLM-based tooling, cutting time-to-remediate by about 5%. He argues least privilege, session-based RBAC via OIDC/OBO, and human-in-the-loop oversight remain the bedrock defenses for AI agents, and that hiring should favor engineers able to catch confidently wrong AI output.

Help Net Security05:30 UTC · 4h agoIndustry

Data Broker Radaris Loses Domains in Privacy Fight

A New Jersey court ordered people-search broker Radaris to transfer radaris.com and a dozen related domains to Atlas Data Privacy over Daniel's Law violations.

On August 26, a New Jersey judge found Radaris failed to defend claims that it violated Daniel's Law, which protects law enforcement officials' personal data and imposes $1,000 fines per ignored removal request. The court ordered radaris.com and more than a dozen related broker domains transferred to plaintiff Atlas Data Privacy Corp. Radaris had delayed litigation using offshore shell entities and previously used a fictitious CEO named 'Gary Norden' in investor-facing press releases.

Krebs on SecuritySep 16, 18:14 UTC · 16h agoPolicy & legal

Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs

Researchers present incremental KV-cache memory maintenance for long-lived game NPCs running locally on a quantized Qwen hybrid model.

The paper studies incremental memory maintenance for long-lived game NPCs deployed locally with a quantized Qwen hybrid recurrent-attention language model. The runtime removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV. Experiments across eight scripted maintenance rounds show true-tail updates preserve current-state and historical bindings, while slot-preserving alternatives repeat a double-subtraction error.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:09 UTC · 17h agoAI research

A brief history of AI executives calling for regulation

The Verge chronicles the history of AI executives, from Samuel Butler and Turing-era warnings to Altman and Musk, publicly calling for AI regulation.

The article traces recurring calls for AI regulation, from Samuel Butler's 1863 warnings and Alan Turing's 1951 lecture to Bill Joy's 2000 essay and Microsoft's 2018 facial recognition stance. Modern examples include Elon Musk's 2017 remarks to US governors, the 2023 Future of Life Institute pause letter, and Sam Altman's 2023 Senate testimony. It argues such appeals from industry leaders who profit from AI warrant skepticism.

The Verge · AISep 16, 12:00 UTC · 22h agoAI policy1

Robot Visions: Breaking reCAPTCHA at Zero Cost and Zero Shot

Researchers defeat Google reCAPTCHA using free local models CLIP and OWLv2, achieving 92.6% per-session success at zero cost.

The paper taxonomizes Google reCAPTCHA challenges into Type A (independent tiles) and Type B (4x4 grid) and builds zero-shot, training-free solvers from open-source local models. CLIP solves 58% of Type A challenges and OWLv2 43.5% of Type B, while an end-to-end automated solver succeeds on 92.6% of 500 real-world sessions. The authors also show a non-technical adversary can solve challenges using natural-language instructions to a commodity AI assistant, collapsing the attacker skill floor and suggesting visual challenge CAPTCHAs have reached the end of their useful life.

arXiv cs.CRSep 16, 11:48 UTC · 22h agoAI safety & security