JustinPerea · JustinPerea · May 7, 2026
diff --git a/_meta/claims-ledger.md b/_meta/claims-ledger.md
@@ -9,3 +9,11 @@
 - **2026-05-04** [hf-blog-2026-04-29-ibm-granite-4-1] CLAIM: Granite 4.1-8B "consistently matches or outperforms" Granite 4.0-H-Small (32B MoE / 9B active); 30B reports BFCL v3 73.7, RULER@128K 76.7, GSM8K 94.2, HumanEval 89.6 | source: https://huggingface.co/blog/ibm-granite/granite-4-1 | status: open
 - **2026-05-04** [hf-blog-2026-04-29-evaleval-eval-costs-bottleneck] CLAIM: agent benchmarks compress only 2–3.5× vs. 100–200× for static; single GAIA frontier-model run ~$2,829; HAL k=8 reliability rerun ~$320K; pass^k drops from 60% (k=1) to 25% (k=8) | source: https://huggingface.co/blog/evaleval/eval-costs-bottleneck | status: open
 - **2026-05-04** [anthropic-research-2026-04-29-biomysterybench] CLAIM: Opus 4.6 ~77% / Sonnet 4.6 ~66% on 76 human-solvable BioMysteryBench tasks; "Claude Mythos Preview" 30% on 23 human-difficult tasks (vs. Opus 4.6 ~24%); many hard-set wins are "brittle" (inconsistent across 5 attempts) | source: https://www.anthropic.com/research/Evaluating-Claude-For-Bioinformatics-With-BioMysteryBench | status: open ; first public reference to "Claude Mythos Preview" model handle
+- **2026-05-07** [arxiv:2605.05191] CLAIM: LongSeeker (Qwen3-30B-A3B fine-tune) with five context-orchestration ops (Skip/Compress/Rollback/Snippet/Delete) achieves 61.5% BrowseComp / 62.5% BrowseComp-ZH, vs. Tongyi DeepResearch 43.2/46.7 and AgentFold 36.2/47.3; Compress operator proven expressively complete | source: https://arxiv.org/abs/2605.05191 | status: open
+- **2026-05-07** [arxiv:2605.05066] CLAIM: No architecture can simultaneously achieve length-independent per-step compute, length-independent state, and length-proportional recall; models satisfying first two recall at most O(poly(d)/log V) KV pairs; verified across 52 architectures pre-March 2026 | source: https://arxiv.org/abs/2605.05066 | status: open ; foundational theoretical claim, expect citations rather than refutations
+- **2026-05-07** [arxiv:2605.05007] CLAIM: Uno-Orchestra (learned joint policy over decomposition + worker selection + budget) hits 77.0% macro pass@1 across 13 benchmarks, +16 pp over strongest workflow baseline at ~10× lower per-query cost | source: https://arxiv.org/abs/2605.05007 | status: open ; relates-to [arxiv:2604.27221] (learned vs. hand-designed orchestrator, both report large gains)
+- **2026-05-07** [arxiv:2605.04785] CLAIM: AgentTrust pre-execution tool-call gate achieves 95.0% verdict accuracy / 73.7% risk-level accuracy on 300-scenario internal benchmark; 96.7% / ~93% on 630-scenario adversarial benchmark; ms-latency; ships as MCP server (AGPL-3.0) | source: https://arxiv.org/abs/2605.04785 | status: open ; relates-to [arxiv:2605.00136] tool-use tax (both intervene at protocol layer; safety vs. performance angles)
+- **2026-05-07** [arxiv:2605.04361] CLAIM: Same context artifacts produce up to 20× speedup or 46% degradation across 10 tasks × 7 conditions × 2,700+ runs; baseline (no-context) exploration predicts outcome with Pearson r = -0.82 (p < 0.001); two regimes — training-data-driven (disrupted by artifacts) vs. explicit-instruction-driven (not) | source: https://arxiv.org/abs/2605.04361 | status: open
+- **2026-05-07** [arxiv:2605.05170] CLAIM: Design Conductor 2.0 multi-agent system (frontier models, April 2026) autonomously designed FPGA-mapped TurboQuant inference accelerator (240-cycle pipeline, 5,129 FP16/32 units, 5.7 mm² in TSMC 16FF) in 80 hours; claims 80× larger task scope than prior 12h RISC-V CPU result | source: https://arxiv.org/abs/2605.05170 | status: open ; vendor-self-reported, autonomy claim awaits independent reproduction
+- **2026-05-07** [hf-blog-2026-05-06-vllm-correctness-before-corrections] CLAIM: When migrating PipelineRL from vLLM 0.8.5 → 0.18.1, logprob mismatches between inference and training masquerade as RL-objective divergence; required fixes (logprobs_mode=processed, disable prefix caching, disable async scheduling, fp32 lm_head, specific weight-update sequence) restore V0 parity on policy ratio, KL, entropy, reward, weight lag | source: https://huggingface.co/blog/ServiceNow-AI/correctness-before-corrections | status: open
+- **2026-05-07** [anthropic-news-2026-05-05-finance-agents] CLAIM: Claude Opus 4.7 scores 64.37% on Vals AI Finance Agent benchmark | source: https://www.anthropic.com/news/finance-agents | status: open ; vendor-self-reported, first public Opus 4.7 number tracked in this ledger
diff --git a/_meta/processed.json b/_meta/processed.json
@@ -1,4 +1,12 @@
 [
+  { "id": "arxiv:2605.05191", "url": "https://arxiv.org/abs/2605.05191", "title": "LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents", "date_covered": "2026-05-07" },
+  { "id": "arxiv:2605.05066", "url": "https://arxiv.org/abs/2605.05066", "title": "The Impossibility Triangle of Long-Context Modeling", "date_covered": "2026-05-07" },
+  { "id": "arxiv:2605.05007", "url": "https://arxiv.org/abs/2605.05007", "title": "Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation", "date_covered": "2026-05-07" },
+  { "id": "arxiv:2605.04785", "url": "https://arxiv.org/abs/2605.04785", "title": "AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use", "date_covered": "2026-05-07" },
+  { "id": "arxiv:2605.04361", "url": "https://arxiv.org/abs/2605.04361", "title": "When Context Hurts: The Crossover Effect of Knowledge Transfer on Multi-Agent Design Exploration", "date_covered": "2026-05-07" },
+  { "id": "arxiv:2605.05170", "url": "https://arxiv.org/abs/2605.05170", "title": "Design Conductor 2.0: An agent builds a TurboQuant inference accelerator in 80 hours", "date_covered": "2026-05-07" },
+  { "id": "hf-blog-2026-05-06-vllm-correctness-before-corrections", "url": "https://huggingface.co/blog/ServiceNow-AI/correctness-before-corrections", "title": "vLLM V0 to V1: Correctness Before Corrections in RL", "date_covered": "2026-05-07" },
+  { "id": "anthropic-news-2026-05-05-finance-agents", "url": "https://www.anthropic.com/news/finance-agents", "title": "Agents for financial services", "date_covered": "2026-05-07" },
   { "id": "arxiv:2605.00737", "url": "https://arxiv.org/abs/2605.00737", "title": "To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling", "date_covered": "2026-05-04" },
   { "id": "arxiv:2604.27221", "url": "https://arxiv.org/abs/2604.27221", "title": "Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction", "date_covered": "2026-05-04" },
   { "id": "arxiv:2605.00425", "url": "https://arxiv.org/abs/2605.00425", "title": "AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning", "date_covered": "2026-05-04" },