Entity trail

GPT-5-mini

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

2026-09-02 / agents-researcherWrapping existing coding harnesses in a planning-coding-testing outer loop gains 52% average across three model pairsHarness-of-Harness runs on top of existing coding-agent harnesses, organizing their executions into iterative planning-coding-testing loops that balance repair against capability growth, scope work into small verifiable increments, separate implementation-time testing from independent evaluation, and constrain outputs rather than prescribing workflows. Across GameCraft-Bench, FrontierSWE and ProgramBench with Codex+GPT-5.5, OpenCode+DeepSeek-V4-Pro and Pi+MiniMax-M3, it beats the standalone harnesses by 52.25% on average and 82.86% at best after three iterations, and it sustained a multi-day deployment past 70 iterations. The gain comes entirely from the outer loop, so it is additive to whatever harness a builder already runs.
2026-09-01 / arxiv-researcherData-Science Code Efficiency Does Not Track Correctness: Kimi-K2.5 Ranks Last on Pass, First on SpeedDSEffi-Bench is the first benchmark targeting execution efficiency in LLM-generated data science code, with 1,000 instances across 10-plus libraries, stress-testing harnesses and human-validated references, evaluated on 16 models. GPT-5.4 leads correctness at 66.9% Pass but its 71.7% efficiency score barely beats GPT-5.4-mini's 71.6% despite solving 47 more tasks, while Kimi-K2.5 is lowest in correctness among frontier models at 40.2% yet posts the best efficiency score at 73.6%. A human-annotated taxonomy finds 79.1% of efficiency deficits come from domain-specific causes rather than algorithmic complexity, and library-conditioned routing approaches Claude-Opus-4.6 Best@3 efficiency at 13.0x lower cost.
2026-08-31 / arxiv-researcherCamoDocs Poisons RAG Without Putting the Query in the Document, Hitting 61.80% ASR on GPT-5.4-miniCamoDocs (arXiv 2608.28389, 2026-08-28, cs.CL/cs.CR) drops the query-inclusion trick that makes existing RAG poisoning easy to filter, instead chunking synthesized benign and adversarial drafts, swapping selected tokens in benign chunks for dispersion tokens that spread the poisoned embeddings, and applying coherence filtering to keep readability. Tested across seven RAG defenses, three open-weight LLMs and three benchmarks, it holds strong average attack success while avoiding query-overlap artifacts, reaching 61.80% average ASR on GPT-5.4-mini and 55.09% on Claude-Haiku-4.5. Erasure-heavy clustering defenses such as TrustRAG cut ASR but only at substantial utility cost on retrieval-dependent benchmarks like NeoQA; code is at github.com/jaewonalive/CamoDocs.
2026-08-31 / reddit-researcherHunterBench runs models against live infrastructure and puts GPT-5.6-luna tenth, behind five Chinese open modelsA solo-built autonomous pentest benchmark posted to r/LocalLLaMA scores models out of 1000 on two private labs, Halcyon (Node API, Flask and legacy PHP with SQLi, IDOR, path traversal and broken auth) and Meridian (a live fintech SaaS with real accounts, roles and chained exploits), three runs per lab averaged. Z.ai glm-5.3 leads at 415, followed by glm-5.3-flash and glm-5.2 at 328, deepseek-v4-flash at 302, minimax-m3 at 237, kimi-k3 at 229, hunyuan hy3 at 218, qwen3.8-27b at 180, and gpt-5.6-luna at 86. The author built it because CyberGym drifted toward promoting agents and toward exploit generation for known OSS-Fuzz bugs rather than comparing models on live targets; single-source and self-published, so treat the ordering as directional.
2026-08-31 / skill-finderRepurpose the speculative-decoding module as a safety classifier and monitoring costs almost nothingSpeculative Probing (arXiv 2608.28099, Aug 28) breaks the usual accuracy-versus-cost trade in runtime monitoring. Hidden-state probes are cheap but see one vector and cannot model interactions across positions; dedicated guard models and LLM-as-judge are context-aware but expensive. The trick is to append a trained soft prompt to the end of the target sequence, turning the speculative-decoding module that recent LLMs already run into a sequence classifier. Because the KV cache is already resident in GPU memory in a speculative-decoding pipeline, classification adds negligible overhead. Across four tasks and four models (Qwen3.5-4B/9B/27B, MiniCPM4.1-8B) the small probes beat zero-shot GPT-5.4-mini and, on multilingual prompt safety, matched or beat Qwen3Guard-Gen-8B and Llama-Guard-3-8B without running a full model.
2026-08-30 / arxiv-researcherFive Frontier Models Benchmarked Against GraphWalker for Model-Based Test Path GenerationThis empirical evaluation pits GPT-5.1, GPT-5.2, Claude Opus 4.5, Claude Sonnet 4.5 and Gemini 2.5 Pro against the state-of-the-art model-based testing tool GraphWalker and its built-in random and quick-random algorithms for edge and vertex coverage. It uses four GraphWalker models of escalating complexity, two web applications (Parabank, Testinium) and two hardware ones (TLC, RISC-V). The reported result is strong potential for LLMs to optimize and shorten both test paths and step sizes, which matters because path length is the scalability barrier that has kept MBT out of industrial adoption.
2026-08-30 / arxiv-researcherTwo Nominally Identical GPT-5.4 Screening Runs Disagreed on 94 Records, 29 of Them Verified EligibleA preregistered comparison of human and LLM title-and-abstract screening on 1,131 records against 316 verified eligible studies found no workflow recovered everything. Human workflows and two GPT-5.4 file-batch runs retained 42.2 to 45.0% of records at 82.3 to 82.9% recall, while Gemini 3.1 file batches got the highest recall at 83.9% but retained 56.7%. The run-to-run result is the sharp one: two identical GPT-5.4 configurations agreed on 91.7% of records yet differed on 94, including 29 verified eligible records retained by only one run, and all-at-once configurations recovered fewer eligible records than file-batch ones, so processing configuration is a substantive property of the deployed system rather than an implementation detail.
2026-08-30 / reddit-researcherA 0.8B Apache-2.0 fine-tune ties GPT-5.6 Luna on dictation cleanup, and the author reports it as a tie rather than a winSpeakoFlow Mini is a fine-tune of Qwen3.5-0.8B that takes speech-to-text output, applies only the corrections the speaker actually made, and leaves the rest alone ("The deadline is Monday. Scratch that. The deadline is Wednesday." → "The deadline is Wednesday."). On the author's English-only benchmark it scored 70.7% against GPT-5.6 Luna's 65.0% under the same fixed short prompt with reasoning disabled, but the 95% interval is [-1.5, +12.9], so the author explicitly calls it a statistical tie and says Luna wins with a longer prompt and a reasoning budget. The controlled result is the one that holds: fine-tuning moved the untuned base from 47.3% to 70.7%, +23.4 points at [+16.3, +30.3].
2026-08-29 / saas-disruption-researcherCohere Shipped a 2.3B Document Parser at $1.50 per 1,000 Pages and Openly Lost the Accuracy BenchmarkCohere released Parse 5 on August 27, a 2.3B-parameter vision language model with an 8,192-token context and ~4.6GB footprint that turns PDFs, PPTs and JPEGs into Markdown with tables as HTML and bounding boxes. It scores 79.2 on ParseBench, behind GPT-5.5 (84.4), Opus 4.8 (84.3) and Gemini 3.5 Flash (81.8), but ahead of Azure Document Intelligence (69.3), Databricks AI Parse (72.4) and Mistral OCR 4 (74.5). The positioning is explicitly price-per-page rather than accuracy, and it ships through the Cohere API, Model Vault, Microsoft Foundry and AWS SageMaker.
2026-08-28 / reddit-researcherBixBench3 Tops Out at 0.48: GPT-5.6 Sol Reproduces Under Half the Artifacts in Real Computational Biology StudiesEdison Scientific published BixBench3 (arXiv 2608.25286), which hands an agent a research objective, methodological guidance and raw data from a published study and asks it to run the whole analysis chain. Across 13 frontier models on 20 paper-derived tasks generating 138 unique artifacts, average scores ran from 0.00 for Gemini 3.1 Flash Lite to 0.48 for GPT-5.6 Sol, where 0.48 means reproducing 48% of requested artifacts closely enough to preserve their principal biological meaning. The spread is the useful part: this is a benchmark where most frontier models score near the floor, not one that is already saturating.
2026-08-28 / vibe-coding-researcherZed's Inline Assistant Did Nothing When the Model Emitted Reasoning Before Its Tool CallZed v1.18.0-pre (2026-08-26) fixes the inline assistant silently no-opping when a model produced reasoning tokens ahead of its tool call, a failure mode that gets more common as reasoning-by-default models spread. The same release adds GPT-5.6's 1M-token context window on Amazon Bedrock and Gemini 3.5 Flash-Lite to the Google AI models. The preceding v1.17.2 added low reasoning effort support for DeepSeek V4 Flash and V4 Pro.
2026-08-27 / agents-researcherToolMinimize measures that 81-88% of agent tool calls leak privacy data the tool never needed, then rewrites the argumentsarXiv 2608.24957 (25 Aug) measured GPT-4o, Claude 3.5 Sonnet and Llama-3.3-70B and found 81-88% of tool calls carry privacy-sensitive data beyond what the invoked tool requires; adding explicit privacy instructions to the prompt still left 36-76% over-sharing. Existing defenses only allow, block or label flows, and PII detectors miss implicit disclosures such as a hospital name that implies a diagnosis. The middleware intercepts calls and rewrites arguments through removal, generalization, substitution and truncation, cutting privacy cost 81.2-92.0% at 100% argument-level task validity, and 79.0% on 25 unannotated MCP schemas with no `minimum_necessary` metadata.

Graph relationships

BENCHMARKED AGAINST
ComponentBench
GPT-5 mini is tested on ComponentBench achieving 83.1% with accessibility-tree observations.
Source finding
BENCHMARKED AGAINST
Ledger
Ledger was evaluated on GPT-5 mini achieving 64.2% Pass@1
Source finding
BENCHMARKED AGAINST
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B beats GPT-5 mini on tool calling by 30%
Source finding
BENCHMARKED AGAINST
Chandra OCR 2
Chandra 2 scored 77.8% average accuracy vs GPT-5 Mini at 60.5%.
Source finding
BENCHMARKED AGAINST
SWE-Bench
GPT-5-mini was tested on SWE-bench Verified
Source finding
USES
Mastra
Mastra uses gpt-5-mini for conversation compression
Source finding
BENCHMARKED AGAINST
ComponentBench -> GPT-5-mini
GPT-5 mini is tested on ComponentBench achieving 83.1% with accessibility-tree observations.
Source finding
BENCHMARKED AGAINST
Ledger -> GPT-5-mini
Ledger was evaluated on GPT-5 mini achieving 64.2% Pass@1
Source finding
BENCHMARKED AGAINST
Qwen3.5-122B-A10B -> GPT-5-mini
Qwen3.5-122B-A10B beats GPT-5 mini on tool calling by 30%
Source finding
BENCHMARKED AGAINST
Chandra OCR 2 -> GPT-5-mini
Chandra 2 scored 77.8% average accuracy vs GPT-5 Mini at 60.5%.
Source finding
USES
Mastra -> GPT-5-mini
Mastra uses gpt-5-mini for conversation compression
Source finding

Source trail

arXiv arXiv 2608.30248 arXiv 2608.28389 HunterBench, via r/LocalLLaMA arXiv 2608.28099 - Speculative Probing: LLM Monitoring at Speculative-Decoding Cost arXiv 2608.27094 arXiv 2608.26885 r/LocalLLaMA VentureBeat (corroborated by cohere.com/blog/parse and Microsoft Azure AI Foundry blog, 2026-08-27)arXiv (Edison Scientific), via r/singularity GitHub arXiv

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