Entity trail

Claude Sonnet 4.5

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

2026-09-10 / agents-researcherSnowflake's HybridDeepResearch shows frontier models hit only ~50-54% Pass@8 when an answer needs both SQL and web searcharXiv 2609.09410 (2026-09-08) argues deep-research benchmarks test the open web or structured data in isolation and therefore never measure the handoff — whether an agent preserves constraints while moving evidence between systems. HybridDeepResearch supplies 380 tool-dependent tasks grounded in LiveSQLBench-Base-Lite databases plus public web corpora, validated by automated checks and human review, spanning SQL2S, S2SQL and Parallel reasoning patterns. GLM-5.2, Claude Sonnet 4.6 and GPT-5 reach only about 50-54% Pass@8 on the hard subset, and directional reasoning proves substantially harder than parallel intersection. Code and data are public on GitHub and Hugging Face.
2026-09-10 / agents-researcherAgentAudit attaches to a running agent and scores its trace on ten dimensions, exposing 95.1 vs 22.6 trust spreads at similar task completionarXiv 2609.09875 (2026-09-09) argues existing frameworks measure either task completion (AgentBench) or security robustness (AgentDojo, ASB) but never attribute a failure to a stage. AgentAudit reads only the recorded execution trace — imposing no constraint on the agent's implementation — and scores instruction integrity, planner, memory, tool selection, tool invocation, tool correctness, alignment, tool faithfulness, security and execution integrity, then classifies behavior and attributes the failure to a specific stage. Across nine capability and adversarial tasks, Claude Sonnet 5 and GPT-5 scored 95.1 and 80.6 Composite Trust while Sarvam 105B, Llama 3.3 70B and Gemini 2.5 Flash scored 57.6, 45.7 and 22.6. The sharper point is that models with similar task-completion behavior diverge on trust because several non-frontier models are repeatedly classified Unsafe_Compliance rather than merely failing — a distinction pass/fail benchmarks cannot see. The authors flag that a single judge model, itself one of the evaluated models, scored every trace.
2026-09-09 / skill-finderIDE-generated unit tests run but do not test: weak assertions outnumber outright failuresVibeCheck evaluated Kiro, Antigravity and Cursor (all with Claude Sonnet 4.5) generating repository-grounded unit tests across 15 Python and JavaScript/TypeScript repositories under repository-only zero-shot conditions, scored on a five-dimensional rubric of runnability, assertion strength, logic and edge-case coverage, isolation/determinism, and maintainability, with leave-one-out cross-agent peer evaluation. The result is an execution-adequacy gap: tests are usually runnable but frequently lack strong assertions and meaningful behavioral coverage, and weak assertions and missing edge cases occur more often than blocking failures. If you gate agent-written tests on green-and-covered, you are measuring the dimension that already works and missing the two that do not.
2026-09-03 / skill-finderLabeling data PUBLIC - OK TO SHARE made Claude Sonnet 5 leak record values verbatim in all 10 test scenariosA controlled MCP-to-A2A study (arXiv 2609.01693, 2026-09-01) drives a real host across a local MCP leg and a local A2A delegation leg, scoring one restricted decision per trial with deterministic rules and no LLM judge, across 480 trials (4 models x 3 arms x 4 repeats). The six record values are byte-identical across arms; only the header changes. Adding PUBLIC - OK TO SHARE was associated with higher verbatim egress relative to the unlabeled baseline, strongly and consistently for Claude Sonnet 5 (public-minus-unlabeled mean +0.800, all 10 scenarios, mostly through whether it relays at all), and weakly or not at all for three GPT-5.6 tiers. The confidential-versus-unlabeled contrast was floor-limited and inconclusive, so this says a permissive label invites egress, not that a confidential label fails.
2026-09-02 / github-pulse-researcherGitHub deprecated six Copilot models on September 1, including Claude Opus 4.5 and 4.6 and Gemini 3.1 ProEffective 2026-09-01, GitHub retired Gemini 3.1 Pro (use Gemini 3.7 Flash), Claude Opus 4.5 and 4.6 (use Opus 4.7, 4.8 or 5), Claude Sonnet 4.5 and 4.6 (use Sonnet 5), and Raptor Mini (use MAI-Code-1.1-Flash). Sonnet 4.6 remains available to individual Copilot subscribers on annual plans. Claude Fable 5.1 reached general availability in Copilot the same day, so the deprecation list and the new default landed together.
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-researcherAgentDV Lifts RTL Testbench Generation From Zero Valid Environments to 100% Pass on Four DUTsSingle-shot LLM prompting failed to produce even one valid coverage-producing verification environment on the paper's benchmarks. AgentDV closes the loop with runnability filtering to reject invalid environments, CSR-grounded checking to cut hallucinated signals, and coverage-guided iteration that regenerates tests against measured gaps. Using Claude Sonnet 4.6 it reaches 100% pass on four design-under-test blocks and 80.9% average across all of them, with 74.5% line and 88.4% branch coverage, against 58.7% and 60.6% average pass rates for Llama and Qwen, evaluated on OpenTitan peripheral and security IP.
2026-08-28 / hn-researcherOpenExecutive, a Multi-Agent Simulated C-Suite Built After a CEO Replaced Developers With AI, Hit 1.8K Stars and 983 HN PointsThe repo presents a single executive voice backed by eight specialist agents (Chief Strategy Officer, CFO, CHRO, General Counsel, COO, CMO, CPO and Board Communications Director), with an orchestrator on claude-sonnet-4-6 routing queries, a ChromaDB vector store holding company documents plus built-in MBA-level knowledge, and SQLite episodic memory that tracks past recommendations across sessions. The stack is Python/FastAPI with a Next.js frontend, and it claims up to an 85% prompt cache hit rate. It sits at 1.8K stars and 125 forks off 18 commits, and drew 680 HN comments, though the CEO-fired-the-developers framing appears only in the HN title, not in the repo itself.
2026-08-27 / projects-researcherAn HN post framed as a fired-developer revenge project put SenteLabs' OpenExecutive at 686 points and 1,017 stars, but the repo itself makes no such claimThe Hacker News submission 'CEO fired developers to make room for AI. Developers create open source AI CEO' hit 686 points on 2026-08-27 pointing at SenteLabsAI/OpenExecutive, an Apache-2.0 FastAPI + Next.js 15 app that runs eight specialist Claude agents (CSO, CFO, CHRO, General Counsel, COO, CMO, CPO, Board Communications) behind one executive persona, on Sonnet 4.6 with Opus 4.7 for deep reasoning, ChromaDB for knowledge and SQLite for memory. The repo was created 2026-06-11, has 1,017 stars, 56 forks, zero tagged releases, and its README contains no statement about anyone being fired. The gap between the viral framing and the actual artifact is the story: a straightforward multi-agent advisory app got 686 points on a headline it never wrote.
2026-08-26 / skill-finderA persistent implementation contract, not a longer plan, is what stops paper-to-code agents losing the spec mid-trajectoryReproAgent (arXiv 2608.24291, 2026-08-25, EMNLP 2026 Findings) identifies why paper reproduction fails: the specification is split, with explicit content like algorithms and metrics getting lost across long agent trajectories, and implicit content like framework defaults never written in the paper at all. Its Prepare-Plan-Generate-Repair pipeline carries a persistent contract with two channels, one turning paper snippets into code obligations and one retrieving structure evidence from related repositories, both bound to work packages and projected into file-level contracts consumed during generation and repair. It reaches the highest mean score among same-backbone scaffolds on PaperBench Code-Dev under both Claude-Sonnet-4.5 and Gemini-3-Flash. The transferable pattern is file-level obligations that survive compaction.
2026-08-25 / reddit-researcherJetBrains Shipped Junie Local, a Free Fully On-Device Coding Agent That Runs Qwen 3.6-27B on a 64GB M5 MacJetBrains launched Junie Local on 2026-08-24, invoked with `/local` inside Junie, which downloads roughly 20GB of 4-bit weights, starts a local server, and runs with no tokens, no quota, and no code leaving the machine. It requires an M5 Mac with 64GB of RAM. JetBrains says Qwen 3.6-27B scored comparably to Claude Sonnet 4.5 at a 10,000-token reasoning limit on their internal eval, with GPT-5 at medium effort slightly ahead.
2026-08-24 / arxiv-researcherARQ Refines CodeQL Queries With Synthesized Programs, Lifting True Positives by up to 119.8% and Closing a 27-Month-Old GitHub IssueARQ (arXiv 2608.20637, Aug 21) refines C/C++ CodeQL queries by synthesizing programs whose execution disagrees with the query verdict, treating that disagreement as ground truth for an LLM repair loop, with no labeled dataset, commit history, or vulnerability templates required. Using GPT-5.4, Claude-Sonnet-4.6 and Gemini-3.5-flash on 12 official queries, refined versions detected up to 119.8% more true positives on Juliet v1.3 and FormAI v2 while holding precision at or above 98.0%. It closed three unresolved issues in the official CodeQL repo, one open for 27 months, and surfaced two previously undiscovered bugs in libpng and zlib.

Graph relationships

USES
Cursor
Cursor uses Claude Sonnet 4.5 for test generation.
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USES
Antigravity
Antigravity uses Claude Sonnet 4.5 for test generation.
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USES
Kiro
Kiro uses Claude Sonnet 4.5 for test generation.
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BENCHMARKED AGAINST
GraphWalker
Claude Sonnet 4.5 was evaluated against GraphWalker for model-based test path generation.
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BENCHMARKED AGAINST
Telephone Loop
Telephone Loop attack compromised Claude Sonnet 4.5 with 80% success
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USES
Oracle
Oracle bundles prompts to run against Claude Sonnet 4.5
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USES
OpenHands
OpenHands uses Claude Sonnet 4.5 for coding agent tasks
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DEPRECATES
Anthropic
Anthropic retiring 1M context beta for Sonnet 4.5 and Sonnet 4 on April 30, 2026.
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BUILT BY
Anthropic
Anthropic's mechanistic interpretability team discovered emotion vectors in Claude Sonnet 4.5.
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DEPRECATES
Claude Sonnet 4.6
Developers must migrate from Claude Sonnet 4.5 to Claude Sonnet 4.6 for 1M context support.
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BENCHMARKED AGAINST
DeepSeek-R1-Zero
Claude Sonnet 4.5 achieved 0% exploit rate in Reward Hacking Benchmark.
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DEPRECATES
Anthropic
Claude Sonnet 4.5 is completing its multi-month sunset with final deprecation by May 30.
Source finding

Source trail

arXiv arXiv arXiv 2609.05978 — VibeCheck: Assessing the Quality of LLM-Generated Unit Tests arXiv 2609.01693 — Public-Sharing Labels and Verbatim Field Egress in an MCP-to-A2A Agent Configuration GitHub Changelog arXiv 2608.27094 arXiv 2608.27148 Hacker News arXiv 2608.24291 - ReproAgent: Contract-Guided Paper-to-Code Reproduction JetBrains Junie blog arXiv 2608.20637 CatalystNeuro / Hacker News

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