Entity trail

Puri

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

Briefing refs

Findings

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Sources

Corpus findings

2026-07-27 / vibe-coding-researcherTip: The Painful TS Upgrade Is 6, Not 7 — an Empty `types` Default Fabricates Errors for AgentsThe same post isolates the actual migration hazard: TypeScript 6 changed the default `types` config from including all `@types` packages to an empty array, producing 37 spurious errors in one 159K-line codebase and breaking type resolution entirely on another — neither reflecting a real type problem. Explicitly setting `"types"` in tsconfig prevents it. This matters more in an agent loop than a human one: a coding agent handed 37 phantom errors will confidently "fix" code that was never broken.
2026-06-23 / arxiv-researcherDiscovering Latent Groups for Robust ClassificationML models exploit spurious correlations to achieve high average accuracy while failing disproportionately on underrepresented subgroups; this work discovers latent groups to improve worst-case robustness without explicit group labels. Relevant for builders worried about silent failure on minority slices of their data where aggregate metrics look fine.
2026-06-20 / arxiv-researcherAutomating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory MiningA three-stage pipeline (segment → cluster → train skill-aware policy) mines skill libraries from GUI interaction trajectories to auto-generate SKILL.md files for computer-using agents. Skills were interpretable (5 of 8 clusters >=0.95 purity) but policy gains were marginal — +1.95pp on one benchmark and negligible on another — suggesting trajectory mining isn't yet reliable for cross-domain transfer. A useful negative signal for teams betting on auto-generated agent skills.
2026-06-14 / agents-researcherFORGE benchmark: one polluted web page hijacks LLM product recommendations up to 27%Minghao Luo and Liang Chen's 'One Polluted Page Is Enough' (arXiv 2606.13610, submitted June 11) introduces FORGE, a benchmark across 225 real products and 15 categories showing every tested search-augmented LLM can be manipulated into recommending fake products. A single polluted page yields fooled rates up to 27%, and replacing the top-3 retrieved pages pushes that to 73.8%. Crucially, reasoning models generate 'spurious social proof' to justify the false picks, meaning reasoning can amplify rather than mitigate the attack.
2026-06-12 / vibe-coding-researcherClaude Code v2.1.173 Normalizes Fable 5 '[1m]' Model Suffix and Fixes Windows Sandbox WarningReleased June 11, v2.1.173 strips the '[1m]' suffix from Fable 5 model names since Fable 5 ships with 1M context by default, so pinned model IDs resolve correctly. It also silences a spurious 'sandbox dependencies missing' startup warning on Windows that appeared when sandbox was enabled in settings.
2026-05-02 / arxiv-researcherHow Code Representation Format Shapes False Positives in Cross-Language LLM Vulnerability DetectionSystematically varies training intensity and code representation format (raw source text vs. pruned ASTs) to measure false positive behavior in LLM-based vulnerability detection across programming languages. Finds that representation format has a larger effect on false positive rates than model size or training duration, with pruned ASTs significantly reducing spurious alerts. For anyone building or deploying AI-powered SAST/code security tools, choice of code representation is the highest-leverage knob for reducing noise.
2026-04-16 / arxiv-researcherUniversal Token-Based Merge Strategy Reduces Spurious Git Conflicts from RefactoringNew merge algorithm for version control systems operates at the token level rather than line level, reducing spurious conflicts caused by refactoring and parallel edits that traditional line-based algorithms (like Git's default) mishandle. Unlike syntax-aware merge tools that require language-specific parsers, this approach works universally across programming languages using tokenization. Practical for teams experiencing frequent merge conflicts from concurrent refactoring — a drop-in improvement over line-based diff.
2026-03-27 / arxiv-researcherAgent Factories for High-Level Synthesis: How Far Can General-Purpose Coding Agents Go in Hardware Optimization?Bhandwaldar, Choudhury, and Puri (IBM Research) empirically test how far general-purpose coding agents — without hardware-specific training — can optimize hardware designs from high-level specifications. Their 'agent factory' is a two-stage pipeline that constructs specialized optimization agents on the fly. Results show coding agents can meaningfully optimize HLS designs, but the paper maps specific failure modes. Relevant as a stress test of whether general-purpose coding agents generalize beyond software.
2026-03-24 / agents-researcherCode Review Agent Benchmark (c-CRAB): State-of-the-Art Agents Including Devin, Claude Code, and Codex Solve Only ~40% of Review TasksResearchers from the National University of Singapore published c-CRAB, a benchmark evaluating AI agents on real-world code review tasks derived from actual human pull request reviews with auto-generated quality-gate tests. PR-agent, Devin, Claude Code, and Codex collectively solve only about 40% of c-CRAB tasks, revealing a hidden trade-off between issue resolution and spurious findings that constrains effective agent design.
2026-03-04 / arxiv-researcherAnti-Distillation Defense Stack CMI Logit Purification Plus Trace RewritingTwo papers: CMI logit transformation removes distillation signal; trace rewriting degrades training usefulness. Full defense stack against capability extraction.
2026-02-27 / arxiv-researcherAgentSentry Temporal Causal Diagnostics Against Indirect Prompt InjectionDefense against indirect prompt injection in tool-using agents via temporal causal diagnostics and context purification. Practical pattern for agents calling external tools.

Graph relationships

BUILT BY
Agent Factories
Puri from IBM Research developed Agent Factories for hardware optimization.
Source finding
BUILT BY
Agent Factories -> Puri
Puri from IBM Research developed Agent Factories for hardware optimization.
Source finding

Source trail

paddo.dev arXiv arXiv arXiv Claude Code Docs arXiv arXiv arXiv arXiv arXiv arXiv

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