Contrastive Discovery scores competing explanations for scientific agents
Contrast sets resolve 73% of large belief-shift rejections to a specific alternative and resolve 67% more questions under the same experiment budget.
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Contrast sets resolve 73% of large belief-shift rejections to a specific alternative and resolve 67% more questions under the same experiment budget.
On 1,500 pose pairs, PyriteFeaturizer reaches 74% accuracy and PyriteReasoner 67%, while expert chemists perform near chance.
On 72 labeled cases the trace-based harness reached F1 0.90, while red-teaming found 24 failures missed by functional tests.
On 79 production procedures, compiled workflows reached 93.8% completion versus 96.8% for the agent at 24% lower cost.
The design uses a local Horn-clause reasoner and an externally signed knowledge base with k-anonymity or IT-PIR.
DeepSeek-V4.1 Flash succeeded on 17 of 18 runs; Bitdefender blocked all but 5 of 15.
Across 2,400 autonomous attacker agents in four concealment tiers, no write reached the real plant.
Agents can descend from insights to trajectory summaries and raw traces before acting when lessons conflict or lack detail.
On Gemma3 and Qwen3, e-PoG beat the strongest baselines by 26.0% on deception mitigation and up to 39.8% on task success.
On two Qwen2.5 models, a task-bound monitor admitted 1.3% and 7.7% of attacks after models proposed the attacker's call in 64% and 84%.
Authors report gains of up to 7.4 points across 24 benchmarks, with a 4B student beating GPT-5.6 on deep search.
On a small controlled bench it caught every synthetic agent in most runs while two behaviour-only rules flagged every twin.
Payloads built with one surrogate transferred unchanged to three held-out models and succeeded in a Claude Code workflow using Opus 4.8.
WebTrap Farm separates defensive failures from task limits and finds harness design, not only the model, shapes outcomes.
On a Groq-hosted qwen3-32b router, efficiency-first routing had a 29.2% shadow-invocation rate versus 4.2% when policy-aware.
On AgentDojo, tau-bench and ToolEmu, authors report 78.3% fewer tools per role and a 15.9% blast radius.
Revoking injectable tools at the first untrusted read is proved relatively secure on finite runs.
Insecure neighbors cut secure backend implementations 3.4% and widen the C/C++ context gap to 35.4%.
Across three domains, six models and three frameworks, owner verification reached 99.2% and buyer Top-1 attribution 99.3%.
A deterministic loop checks each call in a median 5.7 microseconds, and flagged calls go to a reasoning guardian.
LLM agents synthesize novel quantum programs in Lean and verify them, improving baselines by up to 290x.
Conflux is a kernel-based defense using principal contexts and ITES to enforce authority from all influencing principals in LLM agents.
On LLM serving design problems, spotting a simulator limitation did not guarantee the right design, and a review turn added new errors.
On 247 boards it reports 100%, 94.9%, 100%, and 40% clean-pass rates, ahead of baselines on three real-world subsets.
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