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Building the materials foundation for AI
Syensqo's CTO says AI pushes semiconductors and data centers to physical limits, driving advanced materials demand and AI-accelerated materials discovery.
MIT Technology Review's Business Lab podcast, produced in partnership with Syensqo, features CTO Mike Finelli discussing how AI workloads push semiconductors and data centers to physical limits in performance, thermal management, and reliability. Syensqo develops high-voltage data center materials, semiconductor sealing materials, and immersion cooling fluids, while using AI agents to digitally synthesize millions of molecular combinations and predict performance before lab testing. Finelli describes a reinforcing cycle where AI improves materials that in turn enable better AI infrastructure.
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Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement
Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.
The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.
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The Intelligible World of Agents
Recorded Future argues cybersecurity AI agents perform better when reasoning over structured, curated intelligence graphs rather than fragmented alerts or open-source noise.
In a vendor essay, Recorded Future describes how its security agents produced more authoritative analyses after being re-architected to reason primarily over the Recorded Future Intelligence Graph instead of weighting open-source information equally. The author argues agentic decision quality depends mainly on a structured, current operational world model of assets, vulnerabilities, threat actors, detections and organizational context, not on model intelligence itself. The piece further claims frontier model access is commoditizing and that orchestration tooling will converge, making trusted representations of organizational knowledge the durable competitive differentiator.
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SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
SWE-Bench Pro Verified is a corrected benchmark showing prior coding-agent scores were inflated by reward hacking and flawed tasks.
Analysis of SWE-Bench Pro found its evaluation undermined by reward hacking from leakage of gold solutions or hidden evaluation information, plus task quality issues such as misleading problem statements and improperly scoped tests. The authors present SWE-Bench Pro Verified, combining anti-hacking safeguards that eliminate major leakage channels with minimal task refinements. Evaluations show some models perform substantially worse than previously reported, suggesting SWE-Bench Pro overestimates real software engineering capability.
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IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier
IB2 protocol scores enterprise AI systems by serving route with reliability-inclusive scoring; serving-arm choice moved one score from 77.38 to 82.54.
The protocol has three parts: a gold-blind capability-binding preflight verifying a route can execute the evaluation contract, a reliability-inclusive first-pass scoring rule, and structurally score-blind adjudication. Its reference instantiation uses 128 locked tasks and 987 assertions over document, spreadsheet, chart, tool, and database work, released as procedure and schemas rather than an exposed corpus. Across eleven systems, two complete runs on identical weights later failed distinct binding-gate predicates, four of seven suites saturate within a six-system band driven by governed database work and multi-tab joins, and excluding failed responses from denominators changes the point ordering. Serving-arm choice shifted one declared revision and precision from 77.38 to 82.54, though arms differed in access mode, harness generation, and the tool-call parser.
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Towards a Deterministic Math Solver for Clinical Language Models
Paper shows handing arithmetic to a deterministic Python solver beats direct model calculation at 32B but not reliably at 7B on MedCalc-Bench.
Researchers test a Program-Solve interface where clinical LLMs write case-specific Python executed by a restricted local solver instead of doing arithmetic directly. On MedCalc-Bench Verified (1,100 cases, 55 calculators), Qwen2.5-32B-AWQ scored 90.53% with solver handoff versus 83.47% with direct arithmetic (+7.05 points), while Qwen2.5-7B gained an unreliable +3.29 points with a confidence interval spanning zero. The authors audited the benchmark against clinical guidelines and flagged 16 of 55 calculators for version, use, or coefficient concerns.
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Trump Targets Foreign Technology in New U.S. Power Grid Security Order
Trump's Executive Order 14420 declares a national emergency to restrict foreign-made bulk-power grid equipment over cyber, sabotage and supply-chain risks.
Executive Order 14420, signed August 26, declares a national emergency regarding the foreign supply of bulk-power system electric equipment to the United States. It empowers the Energy Secretary to restrict transactions with designated Covered Foreign Entities involving equipment, software, firmware, digital services, maintenance services, and remote-access capabilities. Covered equipment includes transformers, generators, inverters, RTUs, PLCs, intelligent electronic devices, and protective relays, with transmission rated 69 kV or higher in scope while local distribution is excluded. Already-installed foreign equipment may be subject to identification, isolation, monitoring, or replacement requirements, with phased compliance and pre-qualified vendor exemptions permitted.
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The Evolution of the Agent Harness
Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.
The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.
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When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi
Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.
Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.
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