Entity trail

Claude Sonnet 4.6

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.
2026-09-03 / 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.
2026-09-03 / reddit-researcherClaude's thinking bubble is now disappearing entirely on some messages, with no changelog entryA 204-upvote r/ClaudeAI thread reports that after the full thinking chain was compressed to a one-line summary, the thinking bubble now fails to appear at all on some messages, while the reasoning tokens are still billed. The subreddit's auto-generated summary after 30 comments records the consensus as agreement that the behaviour changed and that no announcement accompanied it, and parallel threads ask whether extended thinking was removed from Sonnet 4.6 as well. For anyone parsing thinking blocks programmatically this is a silent contract change, not a UI preference.
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-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 / agents-researcherCrewAI 1.15.18 promotes conversational flows to stable and stops recording failed tasks as successesReleased 27 August, CrewAI 1.15.18 moves conversational flows out of preview: a chat flow can now declare its own state shape, a declaration can name the router's response format, and a conversational declaration accepts crew-style LLM config. The bug fixes matter more for anyone already running it, including task failures being recorded as failures rather than successes, tool results preserved when the final answer is empty, message roles kept when Agent.kickoff receives a conversation, and the flow lifecycle emitted on a suppressed resume. It also maps default Claude Sonnet 4.6 to its 1M context window and raises the Anthropic default max_tokens for large tool calls.
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 / arxiv-researcherFuzzingBrain-Bench Scores Open-Ended Bug Discovery; Claude Opus 4.8 Crashes 60 of 77 Targets but Scores 196/579Sheng et al. argue that predefined-target proof-of-concept benchmarks discard valid crashes an LLM actually found, so their benchmark hands the model an open-source project plus a sanitizer-instrumented harness in a self-contained Docker image and scores distinct crash signatures, capped per challenge and weighted by difficulty. V1 has 77 challenges from 43 projects (36 C, 32 C++, 9 Java/JVM). Claude Opus 4.8 leads with crashes in 60 of 77 challenges for 196 of a possible 579 points; none of Haiku 4.5, Sonnet 4.6, or Opus 4.8 crashed 13 of the challenges. Corpus and harnesses are public on GitHub.
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.
2026-08-24 / 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.
2026-08-23 / hn-researcherA Cost Analysis Puts the Halving Time for Index-60 Intelligence at About 34 Days, With Grok 4.6 Holding the Record at $0.84 Per TaskA CatalystNeuro post that drew 125 points argues LLM cost curves follow a repeating premium-debut-then-commoditization shape per capability tier, with records halving every 4 to 10 weeks. Cited figures as of 2026-08-19: intelligence index 40+ fell 56x between February and August 2026, from Claude Sonnet 4.6 at $1.22 per task to GPT-5.6 Luna at $0.022; index 50+ fell 35x in five months; index 60+ fell 3.8x since June, halving roughly every 34 days, with Grok 4.6 at $0.84 per task against Claude Opus 5 at $2.34 on max effort. The builder implication the author draws is that read-everything workloads flip from uneconomic to routine, and total spend rises anyway.

Graph relationships

USES
AgentDV
AgentDV uses Claude Sonnet 4.6 for RTL testbench generation.
Source finding
USES
OpenExecutive
OpenExecutive runs an orchestrator on claude-sonnet-4.6.
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USES
CrewAI
CrewAI 1.15.18 maps Claude Sonnet 4.6 as a default LLM model.
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RELEASED
Anthropic
Claude Sonnet 4.6 cost $1.22 per task as of 2026-08-19
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BENCHMARKED AGAINST
GPT-5.4
Output conciseness instruction saves approximately 1.5x in cost compared to input compression when tested across nine models.
Source finding
BENCHMARKED AGAINST
Telephone Loop
Telephone Loop attack was detected at 92% rate in Claude Sonnet 4.6
Source finding
BENCHMARKED AGAINST
Gemini 3.1 Pro
Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 but produces hallucinations in 50% of errors vs 38% for Claude Sonnet 4.6.
Source finding
BENCHMARKED AGAINST
MiniMax M2.7
MiniMax M2.7 matches Claude Sonnet 4.6 benchmarks at 1/3 cost
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BUILT BY
Anthropic
Claude Sonnet 4.6 supports 1M token context window
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USES
Claude Research
Claude Research uses Claude Sonnet 4.6 as worker subagents
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USES
Claude Code
Claude Code auto mode uses Sonnet 4.6 classifier for permission decisions
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RELEASED
Anthropic
Anthropic released Claude Sonnet 4.6 with 1M context in March 2026.
Source finding

Source trail

arXiv arXiv arXiv 2609.01693 — Public-Sharing Labels and Verbatim Field Egress in an MCP-to-A2A Agent Configuration r/ClaudeAI GitHub Changelog arXiv 2608.27148 GitHub (crewAIInc/crewAI)Hacker News arXiv 2608.25158 arXiv 2608.20637 CatalystNeuro / Hacker News arXiv 2608.19861 - PolicyGuide: From Guarding One Action to Guiding the Whole Workflow

Graph sources

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