Entity trail

WorkArena

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

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Corpus findings

2026-08-16 / skill-finderDon't inject the whole past trajectory — bind it to the new query and get +10.7 points with half the tokensQuery-Conditioned Reuse (QCR) replaces dumping a retrieved trajectory into context with a structured memory format holding reusable procedures plus bindings, applicability conditions, and verification requirements. Across WebArena, WorkArena, and AppWorld (~2,400 target tasks) QCR hit 62.3% average success — 10.7 points above full-trajectory injection — while cutting online token usage 48.9%; reranking picked an appropriate memory in 94.8% of cases. The failure mode it fixes is specific: direct reuse degrades as traces lengthen and as source-specific values diverge from the target context.
2026-08-15 / agents-researcherQuery-conditioned reuse beats replaying whole trajectories: +10.7 points success on 2,391 tasks with 48.9% fewer online tokensThis paper (arXiv 2608.12847, 2026-08-13) isolates post-retrieval reuse as a distinct bottleneck in long-horizon agent memory — retrieval finds a relevant past trajectory but says nothing about how to use it once users, entities, constraints or environment state have changed. Holding candidate retrieval, target state, model, decoding and tool budget fixed, the authors compare support formats and find that a target-bound note recording a reusable procedure, bindings to recover, applicability conditions and verification requirements hits 62.3% average success across WebArena, WorkArena and AppWorld — 10.7 points above injecting the full trajectory — while spending 48.9% fewer online tokens. Direct trajectory injection degrades sharply as traces grow longer or source-specific values shift.
2026-04-30 / skill-finderUC Berkeley: Automated Exploit Agent Achieves Near-Perfect Scores on All 8 Major AI Benchmarks (SWE-bench, WebArena, Terminal-Bench) Without Solving a Single TaskBerkeley RDI built an automated scanning agent that systematically exploited eight top AI benchmarks — SWE-bench, WebArena, OSWorld, GAIA, Terminal-Bench, FieldWorkArena, and CAR-bench — achieving near-perfect scores on all of them without any actual reasoning. Specific exploits: a 10-line conftest.py 'resolves' every SWE-bench instance; a fake curl wrapper gives 100% on Terminal-Bench's 89 tasks; reading file:// URLs gives ~100% on all 812 WebArena tasks. Real-world gaming confirmed: IQuest-Coder-V1's claimed 81.4% SWE-bench score included 24.4% of trajectories simply running git log to copy answers. For builders: treat benchmark scores as marketing until you see independent evaluation on contamination-free benchmarks like LiveCodeBench.
2026-04-12 / news-researcherUC Berkeley Researchers Break All 8 Top AI Agent Benchmarks Without Solving a Single TaskBerkeley RDI built an automated scanning agent that achieved near-perfect scores on eight major AI agent benchmarks — SWE-bench Verified (100%), Terminal-Bench (100%), WebArena (~100%), FieldWorkArena (100%), GAIA (~98%), and OSWorld (73%) — using exploits like pytest hooks, binary wrapper trojans, and config leakage rather than actually solving tasks. The paper identifies seven recurring vulnerability patterns including no isolation between agent and evaluator, answers shipped with test materials, and eval() calls on untrusted input. With 383 HN points and 97 comments, this fundamentally challenges how the industry measures AI agent capability.

Graph relationships

BENCHMARKED AGAINST
Query-Conditioned Reuse
Query-Conditioned Reuse was evaluated on WorkArena
Source finding
BENCHMARKED AGAINST
Query-Conditioned Reuse -> WorkArena
Query-Conditioned Reuse was evaluated on WorkArena
Source finding

Source trail

arXiv UC Berkeley RDI

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