Entity trail

Hypothesis

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

Briefing refs

Findings

Edges

Sources

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

Corpus findings

2026-09-23 / arxiv-researcherTaste-Bench: Best Frontier Model Picks the Better Direction at Only 59.7% of Long-Horizon Agent Decision ForksTaste-Bench mines decision forks automatically from parallel agent attempts and from detours inside single engineering and research trajectories. At each fork one direction led to a better outcome, and the model must choose without seeing what came after. The best frontier model answered only 59.7% correctly, which isolates judgment on which hypothesis or implementation to pursue as a weak spot that end-to-end success metrics hide.
2026-09-22 / sources-researcherRoboDawn asks whether a VLM can drive a robot through nothing but discrete move commandsThe top-upvoted paper on HuggingFace's September 22 list (93 upvotes) tests whether vision-language model intelligence transfers across the digital-to-physical gap by exposing robotic control to an agentic VLM through a compact set of discrete translation, rotation and gripper commands. The VLM runs a closed loop, observing visual state, reasoning about the next action, executing, then adapting to the result, with an in-context learning scheme that grounds behavior from a handful of demonstrations rather than fine-tuning. The framing matters because it treats embodiment as an interface problem rather than a training problem, which is a cheaper hypothesis than the vision-language-action model route.
2026-09-17 / reddit-researcherA 63-hour autonomous Riemann hypothesis run on one 3090 burned 50M+ tokens and never hallucinated an answerAn r/LocalLLaMA user (391 upvotes) let Qwen3.8 27B at 4-bit with a 100K context window run autonomously for 63 hours on a single RTX 3090 attempting the Riemann hypothesis, consuming over 50 million tokens. It did not solve it, which was never the point; the reported result is that across 63 hours it never fabricated a proof, repeatedly caught and corrected its own errors, and kept generating new attack strategies rather than looping. As a long-horizon agent durability datapoint on consumer hardware that is more interesting than the math, and it is a cheap experiment to replicate.
2026-09-15 / agents-researcherHypoEvolve wraps a genetic algorithm around multi-agent LLMs and beats six baselines on drug-repurposing hypothesis generationA September 14 arXiv paper from 13 authors runs a generational genetic algorithm over specialized LLM agents that separately handle mechanistic argument, assumption reconsideration, and evidence and testability assessment. On drug repurposing evaluated against DepMap and Open Targets across 34 cancer types, it reached 0.171 DepMap selectivity versus 0.115 for the strongest baseline, outperforming six baselines on both measures. Gains over single-pass generation also held on held-out cancer types, which is the part that matters for builders: the evolutionary loop, not the agent specialization alone, carried the improvement.
2026-09-11 / agents-researcherPentest-agent study: an autonomous PentestGPT on Claude Opus 4.8 solves all three targets, and adding memory did not help either systemarXiv 2609.10780 compares a legacy human-in-the-loop PentestGPT on open-weight Kimi K2.5 with a newer autonomous version on Claude Opus 4.8. Across three public targets, the autonomous one solved everything. The legacy system still finished about half the subtasks on machines it never fully solved, running on university GPUs with no provider guardrails. Adding a coverage-memory layer improved neither system. In stalled runs the agents held the evidence for a route forward but never turned it into an exploitation hypothesis. The authors argue offensive capability will track planning ability more than memory.
2026-09-10 / arxiv-researcherHide the Action Log From the Agent and Show It Only a Bayesian PosteriorarXiv 2609.10036 (2026-09-09) traces characteristic LLM agent failures under partial observability (premature commitment on ambiguous feedback, collapsing onto the wrong hypothesis after one observation, policy drift as history grows) to a single structural cause: the agent is a history-conditioned policy with no explicit belief over hidden state. The Belief-State Engine sits outside the LLM, maintains a Bayesian posterior over a POMDP's latent states, and exposes only that posterior while withholding the raw action-observation log. The authors prove soundness as a Markov policy on the induced belief MDP provided the raw history is never shown, and beat six baselines including ReAct, QMDP and POMCP on the Tiger POMDP and a red-team attack-graph task.
2026-09-10 / skill-finderA spec-first agent framework taxonomy: persuasion, front-loaded structure, or controls the agent cannot editConsort characterizes the installable spec-first agent frameworks (GitHub Spec Kit, obra/superpowers, BMAD, GSD) as agreeing on capturing intent up front and differing only in how they enforce engineering discipline, splitting enforcement into three modes: persuasion via prompt discipline the model may ignore, front-loaded structure with strong specs then a trusted build, and controls the agent runs inside but cannot bypass. Consort implements the third, using a deterministic orchestrator driving separate role agents through a spec lane and a TDD build lane, with human-approved gates, immutable tests, and a green result that must pass against a live branched database. The paper frames its claims as a pre-registered testable hypothesis rather than reporting results, so treat it as a design vocabulary for auditing your own setup, not evidence one mode wins.
2026-09-06 / rss-researcherA measured Astra-vs-Sol agent run: medium reasoning was cheapest and fastest, high was neitherA gateway vendor benchmarked a three-phase autonomous coding pipeline using Galley inside the Codex CLI. Astra Medium finished at $25.67 total, 19% under Sol High ($31.79) and 31% under Astra High ($37.23), and completed implementation in 31 minutes against Sol High's 52. The token gap is the interesting part: Sol High burned 37.8M input tokens across 238 requests from repeated context reloading, while Astra Medium finished the same phase on 11.1M input tokens over 80 requests. Single-source and published by a router with an obvious interest in the conclusion, so treat the ranking as a hypothesis to re-run on your own stack.
2026-09-06 / sources-researcher"Large-Language Models as a Cognitive Virus" models LLM adoption as an epidemic with a lock-in tipping pointarXiv 2609.03344 (294 points on Hacker News) applies a viral-transmission model to LLM use, tracking transitions among uncoupled, coupled and persistently dependent users. The claim is that social transmission plus collective reinforcement can produce runaway dynamics: past a critical adoption threshold, small increases trigger population-level shifts into persistent dependence with abrupt losses in cognitive competence. The same framework yields conditions for "cognitive immunization" based on reducing transmission and preserving reversibility. It is a modeling paper, not an empirical one — treat the tipping point as a hypothesis.
2026-09-05 / arxiv-researcherAn Upstream Agent's Wrong Claim Captures 54.2% of Answers When It Arrives FirstarXiv 2609.03425 names the temporal-weight effect, where in AI-to-AI communication whatever arrives first acquires unearned authority, and tests it in a preregistered 1,908-trial experiment on one frontier model. With verification removed, an incorrect upstream claim arriving first captured 54.2% of answers versus 4.2% under full verification, while the same claim arriving after the receiver sealed its own answer captured 31.6%, though the authors note the two prompt shells were not length-matched. Because a registered check of tool use failed its call-budget condition, the registration classifies the round inconclusive and reports every result as exploratory, which is unusually honest and the reason to treat the ordering effect as a hypothesis worth designing multi-agent pipelines against rather than a settled number.
2026-09-04 / github-pulse-researcherreverify hit 850 stars in four days as an MCP server that refuses to report a binary claim it cannot check against the bytes2akouwu/reverify was created 2026-08-31 and is at 850 stars with 184 forks, a 22% fork-to-star ratio and commits landing as of 2026-09-04. It pairs a model with a deterministic pure-Python RE toolkit (PE/ELF/Mach-O parsing, x86/x64/ARM/ARM64 disassembly, AOB scanning, CPU emulation, Protobuf/TLV dissection, Frida hook generation) and makes the toolkit the judge, so every structure or algorithm hypothesis comes back VERIFIED, REFUTED or INCONCLUSIVE with evidence rather than a confident guess. It ships as both MCP server and CLI, is MIT licensed, and is published on PyPI, and today's commits added benchmark records with hashes and a CI gate across three platforms.
2026-09-03 / vibe-coding-researcherBelief-Calibrated Optimization makes the coding-agent-as-optimizer state its hypothesis before editingarXiv 2609.01861 (2026-09-01) targets the now-common loop where a coding agent reads scores and traces and iteratively edits the scaffold around a frozen model. Each edit rests on a belief about how the environment will respond, and that belief is normally implicit, living in the agent's reasoning on the current call. The paper makes the world model explicit and calibrates it, which is directly applicable to anyone running a self-improving harness loop against benchmark scores.

Graph relationships

USES
Peter Lavigne
Peter Lavigne proposes Hypothesis for property-based testing
Source finding
USES
Peter Lavigne -> Hypothesis
Peter Lavigne proposes Hypothesis for property-based testing
Source finding

Source trail

surya-koritala/loomfeed published a case study arXiv arXiv / HuggingFace Daily Papers r/LocalLLaMA arXiv arXiv arXiv 2609.10036 arXiv 2609.09671 — Introducing Consort: A Spec-First Agent Framework for Enforced, Test-Driven Development on Live Database Branches n1n.ai blog arXiv arXiv 2609.03425

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