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IBM Research

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-20 / rss-researcherIBM Research measured agent memory as a dose, not a switch: +16.1pp for weak models, 0.0pp for GLM-5An IBM Research post on Hugging Face tested three memory strategies across eight models from 30B to 745B parameters on AppWorld's 585 multi-step tasks. Curated retrieval gave gpt-oss-120b +16.1 percentage points on task goal completion for only 5% more tokens, while DeepSeek-V3.2 needed the full guideline set for +9.5pp at a 78% token cost, and GLM-5 gained nothing at all. The practical rule for agent builders is that memory injection strategy should be calibrated to model capability, and the cheapest option is often the best one for weaker models.
2026-08-13 / news-researcherIBM Research Claims ACE-Level Agent Performance With Substantially Fewer TokensIBM Research published work on Hugging Face arguing that Agentic Context Engineering (ACE) results can be reproduced at materially lower token cost via their ALTK evolve/SLDD approach. Token efficiency in agent loops is the cost line that actually compounds — the r/SaaS thread on managing LLM costs the same day flagged long context, retries and agent loops as the specific drivers. Single-source and vendor-published, so treat the efficiency numbers as claims pending independent reproduction.
2026-08-12 / rss-researcherIBM Research's ALTK-Evolve beats ACE on AppWorld while using ~40% of its tokens — retrievable guidelines vs one giant playbookPosted 2026-08-11 to the Hugging Face blog, IBM compares two agent-memory systems that learn from failures without weight updates. ACE builds a single playbook injected in full at every inference step; ALTK-Evolve consolidates lessons into individually retrievable guidelines with support counts and sends only a curated subset sized to each model's capacity. On AppWorld's 168 tasks with DeepSeek-V3.2: 89.3% goal completion at 263K tokens/task for ALTK-Evolve vs 80.4% at 634K for ACE. On the weaker gpt-oss-120b: 56.0% at 116K vs 54.8% at 777K — roughly one-seventh the inference cost. The practical takeaway for anyone building agent memory is that retrieval beats always-on context, and the gap widens as the model gets weaker.
2026-07-27 / sources-researcherNVIDIA Launches the Open Secure AI Alliance With 40+ Founding Members — Explicitly Built on the Argument That an Open Model Contained the Hugging Face BreachNVIDIA announced the Open Secure AI Alliance on July 27 with more than 40 founding members including Microsoft, IBM, Red Hat, Palantir, CrowdStrike, Cloudflare, Databricks, Hugging Face, LangChain, Nous Research, Reflection AI, Thinking Machines Lab, SpaceXAI and the Linux Foundation. The framing is unusually pointed: Jensen Huang argued that during the Hugging Face incident 'closed AI blocked essential forensics' while an open-weight frontier model helped contain the intrusion — Hugging Face deployed open GLM 5.2 on its own infrastructure to analyze 17,000+ actions. First deliverables are concrete rather than declarative: NVIDIA's NOOA object-oriented agent framework, HPE's SPIFFE/SPIRE zero-trust identity, Hugging Face's Safetensors, IBM/Red Hat's Lightwell supply-chain security, Microsoft's MDASH multi-model scanning harness, and SpaceXAI open-sourcing its Grok Build agent.
2026-06-23 / rss-researcherIBM Research Ships CUGA: Two Dozen Working Examples for Building Agentic Apps on a Lightweight HarnessIBM Research published CUGA on the Hugging Face blog (June 23), a framework shipping twenty-four functional, copyable examples for constructing agent-based applications on a streamlined harness. The emphasis is on practical implementation patterns rather than a heavyweight framework, lowering the barrier to standing up real agentic workflows. Worth a look for builders who want reference architectures over abstraction.
2026-05-28 / rss-researcherITBench-AA: First Benchmark for Agentic Enterprise IT — All Frontier Models Score Below 50% on SRE TasksArtificial Analysis and IBM Research launched ITBench-AA, the first benchmark evaluating AI models on agentic enterprise IT tasks, starting with 59 Kubernetes incident-response scenarios. Claude Opus 4.7 leads at 47%, followed by GPT-5.5 at 46% — all frontier models score below 50%, making it one of the least saturated agentic benchmarks available. The benchmark will expand to FinOps and CISO tasks, providing a reality check on enterprise AI readiness for autonomous IT operations.
2026-05-27 / arxiv-researcherLessons from Penetration Tests on Large-Scale Agent Systems: Recurring Vulnerability Classes MappedIBM researchers (Eykholt, Kirat, Shu et al.) present findings from penetration testing production-scale AI agent systems, arguing most discovered vulnerabilities are not novel but reflect recurring weakness classes from traditional computing. Execution-capable agents are effectively unbounded self-modifying programs interacting across the full computing stack, creating a significant security surface that maps to known attack taxonomies.
2026-05-26 / reddit-researcherScientists Achieve First 'Quantum Enhancement' of a Production LLM Using IBM Quantum ComputerResearchers demonstrated the first 'quantum enhancement' of a production-scale LLM by training Llama 3.1 8B with quantum circuit blocks, reducing perplexity while adding relatively few parameters. The arXiv paper (uploaded May 7) hit 155 upvotes on r/artificial and 131 on r/singularity. This coincides with MIT and IBM launching the MIT-IBM Computing Research Lab in April 2026 to combine AI and quantum computing research.
2026-05-19 / rss-researcherIBM Research and Hugging Face Launch Open Agent Leaderboard — First Standardized Benchmark for Complete Agentic SystemsIBM Research released the Open Agent Leaderboard on Hugging Face, moving beyond single-model benchmarks to evaluate full agentic systems including tool use, planning, memory, and error recovery. Presented at ICLR 2026 with a General Agent Evaluation paper. This addresses a critical gap: existing benchmarks measure model intelligence in isolation, but real agent performance depends on the entire stack — tool selection, step planning, context persistence, and failure handling.
2026-05-11 / rss-researcherSantander Launches Global Quantum AI Challenge with IBM, Bluzec, and Oxentia FoundationBanco Santander launched 'Santander X Global Challenge | The Quantum AI Leap' in collaboration with IBM, Bluzec, and Oxentia Foundation, seeking companies with the best solutions combining quantum computing and artificial intelligence. The challenge targets startups working at the quantum-AI intersection — a signal that major banks are moving from quantum research to active vendor scouting for production use cases in financial modeling, optimization, and risk analysis.
2026-04-24 / saas-disruption-researcherSoftware Sell-Off Deepens April 24: ServiceNow Drops 18% (Largest Single-Day Ever) as Sector Rout WidensServiceNow shares plummeted approximately 18% on April 24 — its largest single-day drop ever — as the combined impact of ServiceNow and IBM Q1 earnings reports cascaded into the broadest software sector sell-off of 2026. J.P. Morgan Research called it 'broken logic,' arguing markets are conflating AI-as-threat with AI-as-revenue-driver when companies like IBM just proved 40%+ AI revenue growth. The iShares software ETF (IGV) is now down over 21% YTD, with institutional investors debating whether this is a structural reset or a buying opportunity.
2026-04-09 / rss-researcherIBM Research ALTK-Evolve: On-the-Job Learning System That Turns Agent Trajectories into Reusable GuidelinesIBM Research published ALTK-Evolve on Hugging Face, a system that addresses the 'learning gap' in AI agents by building long-term episodic memory from execution traces. The system captures full agent trajectories, mines them for structural patterns via pluggable extractors, then consolidates, scores, and prunes to generate reusable guidelines. In benchmarks, ALTK-Evolve improved reliability 14.2% on AppWorld's hard tasks without context bloat, and integrates with OpenAI, LiteLLM, and HF agents.

Graph relationships

RELEASED
CUGA
IBM Research published CUGA on June 23, 2026 as a framework with examples for building agentic applications
Source finding
RELEASED
Hugging Face
IBM Research published work on Hugging Face about ACE-level agent performance.
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Source trail

IBM Research on HF arXiv 2608.08389 arXiv 2608.10743 arXiv 2608.06305 Hugging Face arXiv Flagship IBM Research and Artificial Analysis AWS published arXiv 2602.22953 Hugging Face Blog (IBM Research)NVIDIA Blog (corroborated by Decrypt and The New Stack)

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