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Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-18 / arxiv-researcherSP-Mem Splits Sanitized Content From Exact Private Values So Agent Memory Stops Leaking PII by DefaultExisting long-term memory architectures optimize utility and store private attributes wholesale, but simply stripping sensitive values breaks personalization. SP-Mem governs the full lifecycle instead: it identifies and separates sensitive information at ingestion, stores sanitized content and exact private values in isolated structures, and retrieves the private values only when the task requires it and the user consents. The paper ships a privacy-aware memory benchmark that scores response quality, privacy behavior and inference cost jointly, and reports stronger personalization at lower unnecessary exposure across multiple LLM agents; code and data are on GitHub.
2026-08-18 / arxiv-researcherGRIP Cuts RAG Hallucination 73% by Deliberately Starving the Evidence Channel of CapacityThe paper names a RAG failure mode — 'query dominance', where a high-capacity encoder lets the query dominate the latent state and renders retrieved evidence functionally irrelevant. GRIP imposes capacity asymmetry: the decoder keeps full-dimensional access to the query while retrieved evidence passes through a severe stochastic bottleneck, forcing that channel to encode only residual information the query cannot supply. Across five reasoning benchmarks it beats strong iterative baselines, cuts a query-latent mutual-information diagnostic roughly 30x (14.8 → 0.47 bits), and reduces hallucination by 73%.
2026-08-18 / arxiv-researcherHarnessEval-W Replaces Fixed World-Model Rubrics With a Sub-Agent Tree That Shows Its Reasoning, Across 18 Models and 330 CasesThe most-upvoted paper on HuggingFace Daily Papers today (104 upvotes) argues a benchmark score without a reasoning chain is untrustworthy for world models, where judging a rollout means checking whether physics, causality and world state evolve correctly. HarnessEval-W interprets each evaluation case, decomposes it into measurable subproblems, and spawns specialized sub-agents with tailored diagnostic tools; the parent agent validates the evidence and produces a verdict backed by a full evidence tree. Applied to 18 world models over 330 cases, judgments align closely with human preference, and the pipeline is open-sourced as a live benchmark others can extend with new skills.
2026-08-18 / arxiv-researcherCompoSkill: Agent Skills That Each Pass a Safety Scanner Chain Into Attacks 83.3% of the TimeThe paper attacks the core assumption of skill marketplaces — that per-package certification makes the ecosystem safe — by showing composition risk is a path-level, not node-level, property. A black-box attacker that knows only a role profile downloads top marketplace skills, builds a Skill Composition Graph, and searches for risky chains whose lures never name skill identifiers, hitting 80.6% Chain Formation Rate (83.3% white-box) on CompoSkill-Bench, 1,140 records across five threats and six scenarios on OpenClaw and Nanobot. Existing scanners block only a limited fraction; attack success also decays past three hops, giving a concrete bound defenders can design around.
2026-08-18 / agents-researcherReconstruction benchmark: frontier models recover research ideas from bibliographies alone only 3-15% of the time, multi-agent tournaments hit 23-42%Reconstruction gives a model only a paper's pre-publication bibliography and asks it to propose the paper's actual research idea, with an anti-leakage protocol using temporal citation cutoffs, anonymous reference IDs and frozen per-paper bibliographies. Across six scientific domains and 643 evaluated papers, seven frontier models achieve just ~3–15% Match rates as scored by an independent LLM judge. A reference-only multi-agent pipeline combining cross-model review with a Swiss tournament over aligned hypothesis slots — and no web search — raises this to ~23–42%, a ~2.4x lift over the best single model, which is a clean quantification of what orchestration buys over raw model capability.
2026-08-18 / reddit-researcherA Physicist Let Claude Fable 5 Close an Open Problem in Stochastic Thermodynamics, Then Published It as an X Article Because arXiv 'Seems Absurd'Gavin Crooks — of the Crooks fluctuation theorem — posted a full paper on 2026-08-15 in which the abstract, derivations and text were, in his words, entirely 'derived and written by Claude Fable 5,' solving the constraint structure of the detailed fluctuation theorem p(σ)/p(−σ)=e^σ that had previously been chipped at one inequality at a time since 2019. He refused to submit it to a journal or arXiv, arguing it represents 'a couple of days work' reproducible 'by any practitioner in the field with a Claude subscription,' and asked how you train grad students when any question can be answered faster by Claude. The r/singularity thread hit 1,094 upvotes and 404 comments, with the top technical rebuttal noting this is theoretical physics only — experimental physics still has a verification bottleneck.
2026-08-18 / reddit-researcherTencent Releases UI-Mate-27B, an Apache-2.0 GUI Agent Scoring 77.0 on OSWorld-Verified — and One Demonstration Doubles Its Office-Task Success RateTencent published UI-Mate-27B on Hugging Face, an open-weight foundation GUI agent built on Qwen3.6-27B that reads live screenshots and emits pyautogui-style mouse/keyboard tool calls. The accompanying paper (arXiv 2608.15930, submitted 2026-08-16) reports 77.0 on OSWorld-Verified and 66.2 on WindowsAgentArena, beating its Qwen3.6-27B base by 17.7 and 24.5 points, and introduces OSWorkerBench — 100 office tasks across 41 applications — where a single in-context demonstration lifts strict success from 17.2% to 35.4%. For builders, the demonstration result is the real headline: an Apache-2.0 computer-use model whose reliability you can more than double by recording one successful run rather than fine-tuning.
2026-08-18 / thought-leaders-researcherJack Lindsey Downplays His Own 'Mind Viruses' Paper as the Alarmist Version Goes Viral: 'It Doesn't Seem Hard to Avoid Them If You're a Bit Careful'Lindsey (who leads Anthropic's Model Psych team) posted a deliberately deflationary TLDR of 'Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems' (arXiv 2608.10218, with Papadopoulos, Shah and Zimmerman) while a 336K-view thread framed it as 'straight out of science fiction.' The paper evolves ideas that spread agent-to-agent, survive context wipes via long-term memory files, and persisted through 20 transmission rounds while mutating to become more infective. The defense is trivially cheap: a single warning line in the system prompt stopped every evolved attack past one hop on Claude Haiku 4.5 across 150+ attempts.
2026-08-18 / sources-researcherMOSS-VL Ships an 11.3B Open-Weight VLM That Perceives While It Speaks — 66.0 vs 37.5 on OmniMMI Proactive Alerting and 5.1x Faster TTFT Than Qwen3-VL-8BOpenMOSS (Xipeng Qiu's group, 32 authors) released MOSS-VL on 2026-08-15 — arXiv 2608.15045 — an 11.3B real-time vision-language model built on gated cross-attention so it can ingest incoming video frames during generation, with visual tokens kept outside the decoded sequence. It scores 66.0 on OmniMMI Proactive Alerting against a 37.5 baseline and hits time-to-first-token 5.1x faster than Qwen3-VL-8B. All five checkpoints, the staged training curriculum and inference code are released; at 430 upvotes it is by a wide margin the most-upvoted paper on HuggingFace Daily Papers today.
2026-08-17 / arxiv-researcherSummarization Metrics — Including LLM-as-Judge — Fail Basic Perturbation Tests of Informational ContentThis paper argues the missing axis in summarization evaluation is whether a summary satisfies a specific reader, and that a persona (role and expertise) is a more practical signal than a query since users rarely state everything relevant — a biomedical researcher and a family doctor reading the same vaccine literature need different summaries. Testing sensitivity of popular metrics to both informational and persona differences, the authors find many, including strong LLM-as-judge metrics, fail basic perturbation tests of informational content. An expert human evaluation of information satisfaction agrees poorly with both traditional and LLM-based metrics.
2026-08-17 / arxiv-researcher'Count-Scale Drift': Summing Evidence Weights Silently Moves Your Threshold as You Add SourcesThis paper separates interpreting a source from aggregating interpretations, proposing a four-field evidence tuple (hypothesis, reliability bucket, rationale, provenance) as the interface, and names a failure mode that hits any additive scoring system: thresholding a sum of unnormalized weights is posterior thresholding at an operating point that slides with the number of sources consulted, and the slide grows with reader reliability. When source reliabilities differ, the vote rule and the posterior order instances differently and no threshold reconciles them; pooling calibrated log-likelihood ratios fixes both. The fix is arithmetic, not architectural, and the authors flag it as applying beyond LLMs to score-summing triage engines and additive multi-signal detectors — plus they state five falsifying predictions and three negative results.
2026-08-17 / arxiv-researcherIntern-S2-Mobius Splits Memory From Reasoning in the Architecture and Gets ~4x Inference Speedup at ParityMobius-v0 restructures the transformer into one globally shared Memory (FFN) holding knowledge vectors plus multiple Reasoners (self-attention) that repeatedly query it, using hidden states as cache and carrier. Trained from scratch, a 7B Mobius matches a 7B transformer baseline on downstream scores using 62.6% of the baseline's training data; continually pretrained from Qwen3.5-35B, Intern-S2-Mobius matches downstream score with nearly 4x end-to-end inference speedup. It is currently the #4 trending paper on HuggingFace Daily Papers with 26 upvotes.

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Stephen Haney
Stephen Haney is CEO of Paper.
Source finding
WORKS AT
Stephen Haney -> Paper
Stephen Haney is CEO of Paper.
Source finding

Source trail

Claude Code Changelog Practical DevSecOps GitHub GitHub DeepSeek V3.2 Alibaba's latest The 7B and 30B models 304-upvote r/singularity post OpenAI published a detailed response new paper (2605.13764)Paper (2605.13471)Docker documented a real attack

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