Entity trail

Sonnet 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

2026-09-14 / arxiv-researcherTelling an LLM Judge to Ignore Citation Formatting Kills the Bias and the Measurement Along With ItTraceJudgeBench audits citation-like artifacts in RAG and agent-workflow evaluation across GPT-5.5, Claude Sonnet 4.6, and DeepSeek V4-Flash, with content-equivalent pairs, citation ablations, correctness conflicts, and human-validated quality gaps. Stronger anti-citation prompts cut worse-cited wins from 50.5% to 0%, but some operating points start converting validated moderate-gap decisions into Ties well before the strict stress-test endpoint, even while correctness-conflict accuracy stays at or above 93.0%. TRACE-style decoupling recovers 96.5-100.0% of better-plain resolution, and the authors argue bias suppression, resolution retention, Tie cost, and protocol cost have to be reported together.
2026-09-12 / hn-researcherSakana AI Ships Fugu Max and Fugu Ultra v2, an Orchestrator That Routes Each Task to the Leanest Model, at $2/$6 per Million TokensSakana AI released Fugu Max and Fugu Ultra v2 on September 11, 2026, priced at $2 per million input and $6 per million output tokens, which the company says undercuts Sonnet 5, GPT 5.6 Terra and Kimi K3 by 40 to 60 percent on output. Fugu is not a single model but an orchestrator trained to hand each task to a pool of open-weights and specialized models; Fugu Max takes best overall score on six benchmarks including Terminal Bench 2.1, GPQAD, AA-LCR, AutomationBench and SWEFish, and expands the cost-performance Pareto frontier on 7 of 10. Fugu Ultra v2 scores 48.3 on Chartography against Claude Opus 5 at 27.3 and Fable 5 at 29.5, and 74.3 on DeepSWE, available now through an OpenAI-compatible API.
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.
2026-09-03 / skill-finderWrapping every tool in a natural-language interface beat frontier models by 6% while cutting API cost 85%HEART (arXiv 2609.01736, 2026-09-01) replaces rigid API-schema invocation with Tool Primitives, where each tool is wrapped in an LLM interface that resolves schemas and executes internally, so tools talk to each other in natural language instead of the agent brokering incompatible output types. It backs this with ToolFace, a repository of 25,519 functions retrieved on demand rather than enumerated in context, plus a Planner, Router, and Verifier. The framework beat SFT baselines by 10% on average and GPT-5.4, Claude-4.6-Sonnet, and Gemini-3.1-Pro by 6% across five benchmarks while cutting API cost up to 85%, and on 50 real-world tasks completed 84% against a 22% average for the three commercial models.
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.
2026-08-31 / rss-researcherCursor's CEO puts OpenAI models at 5% of traffic as Anthropic offers compute to absorb the gapFollowing OpenAI's notice that it will stop serving models through Cursor after SpaceX's $60B acquisition closed August 14, with a proposed shutoff of November 12, 2026, Michael Truell said on August 29 that OpenAI models serve about 5% of Cursor user traffic and that talks are ongoing. Anthropic co-founder Tom Brown replied the same night that Cursor has been a partner since Sonnet 3.5 and that Anthropic will "continue to increase compute to support Claude models in Cursor." The 5% figure is the first quantification of what developers actually lose, and it is small enough that the story is about model-supply as a strategic dependency rather than about broken workflows.
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.
2026-08-29 / hn-researcherCalvin French-Owen Puts a Price on the Small-Model Shift: $0.10 a Day Where Sonnet-Class Cost $1French-Owen's August 26 post argues small models crossed the viability line for consumer apps, using his own daily news personalization eval as the measurement: about $0.10 a run on gpt-5.6-luna at roughly 100 tokens per second, against about $1 on a Sonnet-class model. He names GLM 5.3 as a new point on the efficiency frontier and Fable 5 and 5.6 Sol as the expensive capable tier. His framing for builders is that 95% of business work is responsive routine execution rather than novel problem-solving, so the fast-cheap-good-enough tier addresses most of the actual demand.

Graph relationships

BENCHMARKED AGAINST
Fugu Max
Fugu Max undercuts Sonnet 5 by 40 to 60 percent on output tokens.
Source finding
DEPRECATES
TodoWrite
TodoWrite was removed from Sonnet 5.
Source finding
RELEASED
Anthropic
Anthropic released Sonnet 5 model.
Source finding
BENCHMARKED AGAINST
Opus 5
Sonnet 5 scores better unprotected at 0.93% prompt-injection success than Opus 5.
Source finding
CRITICIZES
Willison
Willison documents that Sonnet 5 performs worse than older versions with custom edit tools
Source finding
BUILT BY
Anthropic
Sonnet 5 is Anthropic's model
Source finding
RELEASED
Anthropic
Sonnet 5 is now the default model for Free and Pro plans at $2 and $10 per million tokens.
Source finding
SUPPORTS
lemans
lemans ran model benchmarks including Sonnet 5.
Source finding
BENCHMARKED AGAINST
Fugu Max -> Sonnet 5
Fugu Max undercuts Sonnet 5 by 40 to 60 percent on output tokens.
Source finding
RELEASED
Anthropic -> Sonnet 5
Anthropic released Sonnet 5 model.
Source finding
CRITICIZES
Willison -> Sonnet 5
Willison documents that Sonnet 5 performs worse than older versions with custom edit tools
Source finding
RELEASED
Anthropic -> Sonnet 5
Sonnet 5 is now the default model for Free and Pro plans at $2 and $10 per million tokens.
Source finding

Source trail

arXiv 2609.12439 Sakana AI 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 arXiv 2609.01736 — Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives GitHub Changelog Daily Guardian (reporting Truell and Brown posts)arXiv 2608.27094 arXiv 2608.27148 calv.info / Hacker News

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