Entity trail

ICLR 2026

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

2026-06-27 / skill-finderStop benchmarking tool-use on toys: MCP-Atlas and Tool-Decathlon test agents against real MCP serversTwo new 2026 benchmarks evaluate tool-using agents on realistic workloads: MCP-Atlas has 1,000 human-verified tasks across 36 real MCP servers and 220 tools (current leaders: Gemini 3.5 Flash 83.6%, Claude Opus 4.8 82.2%), and Scale moved it to a 100-tool-call budget instead of a 20-turn limit. Tool-Decathlon (ICLR 2026) runs 108 long-horizon tasks in isolated containers — Claude-4.5-Sonnet finishes all 108 in ~70 minutes across 10 parallel processes. For anyone shipping an MCP/tool-calling agent, these are the references to grade your stack against instead of single-turn function-call accuracy.
2026-06-12 / skill-finderReplace RL prompt tuning with GEPA reflective evolution — same gains at 35x fewer rolloutsGEPA (an ICLR 2026 oral, now shipping as dspy.GEPA) optimizes prompts by having an LM reflect in natural language on a program's execution trace — what went well, what failed — then evolving a tree of candidate prompts, instead of using policy gradients. Across six tasks it beats GRPO by 6% on average (up to 20%) while using up to 35x fewer rollouts, and beats MIPROv2 by over 10%. Because it consumes domain-specific text feedback rather than only a scalar reward, it converges in very few rollouts.
2026-06-02 / skill-finderTurboQuant: Google Research Ships Near-Optimal KV Cache Compression — 3 Bits Per Coordinate, Zero Accuracy LossGoogle Research and NYU published TurboQuant (presented at ICLR 2026), a two-stage vector quantization algorithm compressing LLM KV caches to 3 bits per coordinate with zero accuracy loss. It combines PolarQuant (rotation-based coordinate transform) with a 1-bit QJL residual correction, achieving 6x memory reduction and up to 8x faster attention on NVIDIA H100 GPUs while operating within ~2.7x of the information-theoretic optimum. Community open-source implementations began appearing in early June 2026 — directly actionable for anyone running inference at scale.
2026-05-26 / rss-researcherHugging Face Publishes Definitive Agent Glossary — Harness vs. Scaffold vs. Agent Finally DefinedSergio Paniego and Aritra Roy Gosthipaty published a practical glossary on May 25 addressing the terminology confusion that peaked at ICLR 2026: Model (LLM, no memory, no loop), Scaffold (behavior-defining layer: system prompt, tool descriptions, context management), Harness (execution layer: calls model, handles tools, decides stopping), Agent (Model + Scaffold + Harness in an environment loop). The piece also formally defines context engineering, skills vs. tools, and sub-agents — terms the field has been using inconsistently across frameworks.
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 / reddit-researcherICLR 2026 Contributions by Country and Institution — 5,300+ Papers Accepted in RioA visualization of ICLR 2026 contributions by country and institution is generating discussion on r/singularity (55 upvotes, 32 comments). ICLR 2026 accepted over 5,300 papers, held in Rio de Janeiro April 23-27. Microsoft had 100+ accepted papers. The data reveals shifting global research dynamics as Chinese institutions increase their share of top-tier ML publications. Paper Copilot maintains detailed authorship and country breakdowns for the full accepted paper list.
2026-05-09 / arxiv-researcherLCM: Deterministic Memory Architecture Outperforms Claude Code on Long-Context TasksLossless Context Management (LCM) shifts memory burden from the model to a deterministic engine using recursive context compression (hierarchical summary DAG) and recursive task partitioning (engine-managed parallel primitives). The LCM-augmented coding agent Volt scores higher than Claude Code on the OOLONG long-context eval at every context length from 32K to 1M tokens using Opus 4.6. Accepted at ICLR 2026.
2026-05-07 / reddit-researcherStop Letting LLMs Edit Your .bib Files — Citation Hallucination Now a Systemic Academic ProblemA 150-upvote r/MachineLearning post warns researchers about LLMs corrupting bibliography files, reporting hallucinated citations with correct titles but wrong authors appearing multiple times per month. This aligns with GPTZero's finding that 20% of ICLR 2026 submissions (60 of 300 sampled from 20,000) contain at least one AI hallucination, and Nature's estimate that tens of thousands of 2025 publications may include AI-fabricated references. The academic community is now treating LLM citation corruption as a research integrity crisis, not an edge case.
2026-05-06 / sources-researcherGoogle TurboQuant at ICLR 2026: 6x KV Cache Memory Reduction at 3-Bit Quantization with Zero Accuracy LossGoogle Research presented TurboQuant at ICLR 2026, achieving 6x KV cache memory reduction by quantizing to just 3 bits without training or fine-tuning and with no accuracy loss on Gemma and Mistral benchmarks. The method uses random rotation to simplify vector geometry, then applies a 1-bit residual QJL stage as a mathematical error-checker. Community implementations in PyTorch (Triton kernels), MLX, and llama.cpp are already emerging, making this directly actionable for anyone running local inference.
2026-05-03 / projects-researcherTurboQuant Community Implementations Hit GitHub — PyTorch, MLX, and llama.cpp Ports of Google's 6x KV Cache Compression (ICLR 2026)Independent developers have built working implementations of Google's TurboQuant paper (ICLR 2026) in PyTorch, MLX (Apple Silicon), and C/CUDA for llama.cpp, despite Google not releasing official code. The algorithm compresses LLM KV caches by 6x at 3-bit precision with 99.5% attention fidelity — no training or fine-tuning required. Works as a post-processing step on Gemma, Mistral, and other open models. The llama.cpp discussion thread is driving practical adoption.
2026-05-02 / sources-researcherApple ParaRNN Achieves 665x Speedup Over Sequential RNN Training — First 7B Classical RNN Competitive with Transformers (ICLR 2026 Oral)Apple's ParaRNN framework casts nonlinear recurrence as a single system of equations solved via Newton's iterations in parallel, achieving a 665x speedup over sequential RNN training. This enabled training the first 7-billion-parameter classical RNNs competitive with transformers on language modeling. Accepted as an ICLR 2026 Oral presentation with code publicly released.
2026-04-30 / agents-researcherICLR 2026: Training AI Agents to Reason Harder Increases Tool-Call Hallucinations — 96% of Enterprises Already Running AgentsAn ICLR 2026 paper found that training models to reason more intensively causes them to hallucinate more of the tools they should call — a direct risk for agentic systems that rely on accurate tool invocation. Separately, OutSystems' 2026 State of AI Development survey found 96% of enterprises now run AI agents, while Deloitte's research shows 47% of enterprise AI users have based at least one major business decision on hallucinated content. The combination of near-universal agent deployment with structurally worsening tool hallucination in advanced reasoning models creates an underappreciated reliability gap.

Graph relationships

MENTIONS
AT-GRPO
AT-GRPO boosts planning to 96-99.5% accuracy in ICLR publication.
Source finding
RELEASED
ACE Framework
ACE Framework was accepted at ICLR 2026.
Source finding
PREDICTS
NousResearch
hermes-agent-self-evolution research presented as ICLR 2026 Oral paper
Source finding
MENTIONS
AT-GRPO -> ICLR 2026
AT-GRPO boosts planning to 96-99.5% accuracy in ICLR publication.
Source finding
RELEASED
ACE Framework -> ICLR 2026
ACE Framework was accepted at ICLR 2026.
Source finding
PREDICTS
NousResearch -> ICLR 2026
hermes-agent-self-evolution research presented as ICLR 2026 Oral paper
Source finding

Source trail

Scale Labs — MCP Atlas Leaderboard / Tool-Decathlon (arXiv 2510.25726)DSPy — Reflective Prompt Evolution with GEPA r/LocalLLaMA / Google Research Hugging Face Blog Hugging Face Blog r/singularity arXiv r/MachineLearning / GPTZero / Nature GitHub Apple Machine Learning Research ICLR 2026 / Asanify News ICLR 2026

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