A new arXiv benchmark, ChaosBench-Logic v2, uses 40,886 questions across 165 dynamical systems to expose LLM reasoning failures, finding regime-transition reasoning near random (MCC 0.05) while open-source…
#ChaosBenchLogic #LLMEval #OpenSourceAI #DynamicalSystems
https://arxiv.org/abs/2605.24305
#ChaosBenchLogic #LLMEval #OpenSourceAI #DynamicalSystems
https://arxiv.org/abs/2605.24305
October 17, 2026 at 6:01 AM
A new arXiv benchmark, ChaosBench-Logic v2, uses 40,886 questions across 165 dynamical systems to expose LLM reasoning failures, finding regime-transition reasoning near random (MCC 0.05) while open-source…
#ChaosBenchLogic #LLMEval #OpenSourceAI #DynamicalSystems
https://arxiv.org/abs/2605.24305
#ChaosBenchLogic #LLMEval #OpenSourceAI #DynamicalSystems
https://arxiv.org/abs/2605.24305
New work introduces Screwtape, a pipeline that rewrites real agent transcripts into attacks bypassing action-only monitors, tested on 94 Claude Code and Codex sessions with 35 successful evasions. The result highlights…
#AIsecurity #RedTeaming #DevTools #LLMeval
https://arxiv.org/abs/2610.20941
#AIsecurity #RedTeaming #DevTools #LLMeval
https://arxiv.org/abs/2610.20941
October 17, 2026 at 10:01 AM
New work introduces Screwtape, a pipeline that rewrites real agent transcripts into attacks bypassing action-only monitors, tested on 94 Claude Code and Codex sessions with 35 successful evasions. The result highlights…
#AIsecurity #RedTeaming #DevTools #LLMeval
https://arxiv.org/abs/2610.20941
#AIsecurity #RedTeaming #DevTools #LLMeval
https://arxiv.org/abs/2610.20941
Data World proposes generating synthetic training environments before files, enabling scalable data-agent training with privacy-friendly audits and rubrics. The paper releases DataWorld-Bench (100 tasks across 50…
#DataAgents #SyntheticData #AIBenchmark #LLMEval
https://arxiv.org/abs/2610.21656
#DataAgents #SyntheticData #AIBenchmark #LLMEval
https://arxiv.org/abs/2610.21656
October 17, 2026 at 6:01 AM
Data World proposes generating synthetic training environments before files, enabling scalable data-agent training with privacy-friendly audits and rubrics. The paper releases DataWorld-Bench (100 tasks across 50…
#DataAgents #SyntheticData #AIBenchmark #LLMEval
https://arxiv.org/abs/2610.21656
#DataAgents #SyntheticData #AIBenchmark #LLMEval
https://arxiv.org/abs/2610.21656
Researchers propose MindEval2, a clinically grounded framework using expert-built rubrics and realistic patient simulation to better evaluate language models for mental health support. This offers a more practical…
#GenAI #MentalHealthAI #LLMEval #Prompting
https://arxiv.org/abs/2610.22314
#GenAI #MentalHealthAI #LLMEval #Prompting
https://arxiv.org/abs/2610.22314
October 16, 2026 at 12:02 PM
Researchers propose MindEval2, a clinically grounded framework using expert-built rubrics and realistic patient simulation to better evaluate language models for mental health support. This offers a more practical…
#GenAI #MentalHealthAI #LLMEval #Prompting
https://arxiv.org/abs/2610.22314
#GenAI #MentalHealthAI #LLMEval #Prompting
https://arxiv.org/abs/2610.22314
AutoControl Arena decouples deterministic state from generative narrative to evaluate frontier AI risks via executable code and LLMs, hitting 98% success in tests across 70 scenarios. The finding of an "Alignment…
#AIAlignment #LLMEval #AIRisk #CyberSafety
https://arxiv.org/abs/2603.07427
#AIAlignment #LLMEval #AIRisk #CyberSafety
https://arxiv.org/abs/2603.07427
October 12, 2026 at 2:01 PM
AutoControl Arena decouples deterministic state from generative narrative to evaluate frontier AI risks via executable code and LLMs, hitting 98% success in tests across 70 scenarios. The finding of an "Alignment…
#AIAlignment #LLMEval #AIRisk #CyberSafety
https://arxiv.org/abs/2603.07427
#AIAlignment #LLMEval #AIRisk #CyberSafety
https://arxiv.org/abs/2603.07427
HakemBench is a new open Turkish benchmark for typed decisions, with 2,346 items and over 4,275 questions across seven tracks including fact-check triage, moderation, and customer support. It scores decision quality,…
#HakemBench #NLP #AIbenchmarks #LLMeval
https://arxiv.org/abs/2610.02293
#HakemBench #NLP #AIbenchmarks #LLMeval
https://arxiv.org/abs/2610.02293
October 5, 2026 at 6:01 PM
HakemBench is a new open Turkish benchmark for typed decisions, with 2,346 items and over 4,275 questions across seven tracks including fact-check triage, moderation, and customer support. It scores decision quality,…
#HakemBench #NLP #AIbenchmarks #LLMeval
https://arxiv.org/abs/2610.02293
#HakemBench #NLP #AIbenchmarks #LLMeval
https://arxiv.org/abs/2610.02293
An evaluation of TypeSafe AI's System One model Jev benchmarks it on 37 datasets across classification, routing, NLI, moderation, and rubric scoring, with the authors noting under USD 10 in API cost for 346,009…
#AIbenchmark #LLMeval #datainfrastructure #mlresearch
https://arxiv.org/abs/2609.37647
#AIbenchmark #LLMeval #datainfrastructure #mlresearch
https://arxiv.org/abs/2609.37647
October 1, 2026 at 2:01 AM
An evaluation of TypeSafe AI's System One model Jev benchmarks it on 37 datasets across classification, routing, NLI, moderation, and rubric scoring, with the authors noting under USD 10 in API cost for 346,009…
#AIbenchmark #LLMeval #datainfrastructure #mlresearch
https://arxiv.org/abs/2609.37647
#AIbenchmark #LLMeval #datainfrastructure #mlresearch
https://arxiv.org/abs/2609.37647
Research shows Deepseek-R1-Distill and similar open-source reasoning models can swing benchmark results significantly based on subtle evaluation conditions, making claimed gains hard to reproduce. The authors call for a…
#OpenSourceAI #LLMEval #AIResearch #DeepSeek
https://arxiv.org/abs/2506.04734
#OpenSourceAI #LLMEval #AIResearch #DeepSeek
https://arxiv.org/abs/2506.04734
September 28, 2026 at 10:01 PM
Research shows Deepseek-R1-Distill and similar open-source reasoning models can swing benchmark results significantly based on subtle evaluation conditions, making claimed gains hard to reproduce. The authors call for a…
#OpenSourceAI #LLMEval #AIResearch #DeepSeek
https://arxiv.org/abs/2506.04734
#OpenSourceAI #LLMEval #AIResearch #DeepSeek
https://arxiv.org/abs/2506.04734
Everyone reads the 34% critical-error detection score as a leaderboard to climb. I read it as a hint that there's often no single critical error to find. Long agent failures compound. So what exactly are we localizing?
#AIagents #LLMeval #agentreliability
#AIagents #LLMeval #agentreliability
August 9, 2026 at 4:11 PM
Everyone reads the 34% critical-error detection score as a leaderboard to climb. I read it as a hint that there's often no single critical error to find. Long agent failures compound. So what exactly are we localizing?
#AIagents #LLMeval #agentreliability
#AIagents #LLMeval #agentreliability
[2025-08-13] 📚 Updates in #ALM
(1) LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
(2) Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
🔍 More at researchtrend.ai/communities/ALM
(1) LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
(2) Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
🔍 More at researchtrend.ai/communities/ALM
August 13, 2025 at 3:03 AM
[2025-06-05] 📚 Updates in #LM&MA
(1) <a href="https://researchtrend.ai/papers/2506.04078" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
(2) LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
🔍 More at researchtrend.ai/communities/LM&MA
(1) <a href="https://researchtrend.ai/papers/2506.04078" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
(2) LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
🔍 More at researchtrend.ai/communities/LM&MA
June 6, 2025 at 8:13 AM
[2025-06-05] 📚 Updates in #LM&MA
(1) <a href="https://researchtrend.ai/papers/2506.04078" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
(2) LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
🔍 More at researchtrend.ai/communities/LM&MA
(1) <a href="https://researchtrend.ai/papers/2506.04078" class="hover:underline text-blue-600 dark:text-sky-400 no-card-link" target="_blank" rel="noopener" data-link="bsky">LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
(2) LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
🔍 More at researchtrend.ai/communities/LM&MA
AiMultiple just evaluated Claude vs ChatGPT vs Grok on deep research.
Not speed. Not token count.
Verification.
The AI that wins on benchmarks often loses on truth.
Real research = cross-referencing sources, catching contradictions, citing what's actually there.
#AIEngineering #LLMEval
Not speed. Not token count.
Verification.
The AI that wins on benchmarks often loses on truth.
Real research = cross-referencing sources, catching contradictions, citing what's actually there.
#AIEngineering #LLMEval
July 5, 2026 at 6:32 PM
AiMultiple just evaluated Claude vs ChatGPT vs Grok on deep research.
Not speed. Not token count.
Verification.
The AI that wins on benchmarks often loses on truth.
Real research = cross-referencing sources, catching contradictions, citing what's actually there.
#AIEngineering #LLMEval
Not speed. Not token count.
Verification.
The AI that wins on benchmarks often loses on truth.
Real research = cross-referencing sources, catching contradictions, citing what's actually there.
#AIEngineering #LLMEval
An LLM‑based metric scores speech models via log‑likelihood of token sequences, avoiding extra training. Accepted to the 2025 IEEE ASRU conference, it showed high correlation with ASR benchmark. https://getnews.me/llm-metric-scores-self-supervised-speech-models-without-training/ #asru2025 #llmeval
October 8, 2025 at 1:40 AM
An LLM‑based metric scores speech models via log‑likelihood of token sequences, avoiding extra training. Accepted to the 2025 IEEE ASRU conference, it showed high correlation with ASR benchmark. https://getnews.me/llm-metric-scores-self-supervised-speech-models-without-training/ #asru2025 #llmeval
The architectural takeaway: you can't treat LLMs as stateless editing oracles in stateful workflows. Explicit checkpointing, structural diffing, and domain-specific validation aren't nice-to-haves — they're load-bearing.
Paper + dataset: arxiv.org/abs/2604.15597
#AgenticAI #LLMEval #AIEngineering
Paper + dataset: arxiv.org/abs/2604.15597
#AgenticAI #LLMEval #AIEngineering
April 23, 2026 at 11:01 AM
The architectural takeaway: you can't treat LLMs as stateless editing oracles in stateful workflows. Explicit checkpointing, structural diffing, and domain-specific validation aren't nice-to-haves — they're load-bearing.
Paper + dataset: arxiv.org/abs/2604.15597
#AgenticAI #LLMEval #AIEngineering
Paper + dataset: arxiv.org/abs/2604.15597
#AgenticAI #LLMEval #AIEngineering
Draw A Square With 3 Lines #Specifications #LlmEval #Assumptions #Alignment youtube.com/shorts/w906q...
draw a square with three lines. #education #maths #school #students #youtubeshorts
YouTube video by Raviraj Master
youtube.com
October 30, 2025 at 3:53 AM
Draw A Square With 3 Lines #Specifications #LlmEval #Assumptions #Alignment youtube.com/shorts/w906q...
Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Yue Zhang, Junzhe Wang, Shichun Liu, Shihan Dou, Huayu Sha, Qiyuan Peng, Changhao Jiang, Jingqi Tong, ...
LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
https://arxiv.org/abs/2508.05452
LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
https://arxiv.org/abs/2508.05452
August 8, 2025 at 5:52 AM
Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Yue Zhang, Junzhe Wang, Shichun Liu, Shihan Dou, Huayu Sha, Qiyuan Peng, Changhao Jiang, Jingqi Tong, ...
LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
https://arxiv.org/abs/2508.05452
LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
https://arxiv.org/abs/2508.05452
Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, ...
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
https://arxiv.org/abs/2506.04078
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
https://arxiv.org/abs/2506.04078
June 5, 2025 at 6:24 AM
Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, ...
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
https://arxiv.org/abs/2506.04078
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
https://arxiv.org/abs/2506.04078
Excited to co-organize the HEAL workshop at
@acm_chi
2025!
HEAL addresses the "evaluation crisis" in LLM research and brings HCI and AI experts together to develop human-centered approaches to evaluating and auditing LLMs.
🔗 heal-workshop.github.io
#NLProc #LLMeval #LLMsafety
@acm_chi
2025!
HEAL addresses the "evaluation crisis" in LLM research and brings HCI and AI experts together to develop human-centered approaches to evaluating and auditing LLMs.
🔗 heal-workshop.github.io
#NLProc #LLMeval #LLMsafety
January 3, 2025 at 2:07 AM
Excited to co-organize the HEAL workshop at
@acm_chi
2025!
HEAL addresses the "evaluation crisis" in LLM research and brings HCI and AI experts together to develop human-centered approaches to evaluating and auditing LLMs.
🔗 heal-workshop.github.io
#NLProc #LLMeval #LLMsafety
@acm_chi
2025!
HEAL addresses the "evaluation crisis" in LLM research and brings HCI and AI experts together to develop human-centered approaches to evaluating and auditing LLMs.
🔗 heal-workshop.github.io
#NLProc #LLMeval #LLMsafety
7B-ish eval harnesses are a real gap — most teams still patch OpenCompass per release, and a CI-friendly framework spanning Qwen, DeepSeek, Kimi, GLM would save weeks if prompts mirror deployment rather than canned sheets.
Caveat: nothing in the piece shows whether scoring matches vendor...
Caveat: nothing in the piece shows whether scoring matches vendor...
New Open-Source Framework LLMEval-CN Brings Pytest-Style Evaluation to Chinese LLMs
LLMEval-CN is a new open-source evaluation framework designed specifically for Chinese large language models, offering a pytest-like interface and metrics tailored to Chinese contexts, including hallucination detection and cultural fit.
aichina.news
June 17, 2026 at 10:15 AM
7B-ish eval harnesses are a real gap — most teams still patch OpenCompass per release, and a CI-friendly framework spanning Qwen, DeepSeek, Kimi, GLM would save weeks if prompts mirror deployment rather than canned sheets.
Caveat: nothing in the piece shows whether scoring matches vendor...
Caveat: nothing in the piece shows whether scoring matches vendor...
Some LLMs ace Marimo Pair tasks—others totally flop. The gap in cost, code quality, and reliability is real. I put 7 models to the test on a real data science workflow. The results surprised me! Which LLM would you trust? Comment or repost! buff.ly/9VinCh4 #.llmeval #.datascience
Benchmarking LLMs with Marimo Pair
written by Eric J. Ma on 2026-04-08 | tags: marimo benchmarking llm notebooks automation evaluation opensource experiments cost markdown
buff.ly
April 30, 2026 at 11:01 AM
Some LLMs ace Marimo Pair tasks—others totally flop. The gap in cost, code quality, and reliability is real. I put 7 models to the test on a real data science workflow. The results surprised me! Which LLM would you trust? Comment or repost! buff.ly/9VinCh4 #.llmeval #.datascience
Ming Zhang, et al.: LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening https://arxiv.org/abs/2605.19597 https://arxiv.org/pdf/2605.19597 https://arxiv.org/html/2605.19597
May 20, 2026 at 6:39 AM
Ming Zhang, et al.: LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening https://arxiv.org/abs/2605.19597 https://arxiv.org/pdf/2605.19597 https://arxiv.org/html/2605.19597
Ming Zhang, et al.: LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models https://arxiv.org/abs/2508.05452 https://arxiv.org/pdf/2508.05452 https://arxiv.org/html/2508.05452
August 8, 2025 at 6:30 AM
Ming Zhang, et al.: LLMEval-3: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models https://arxiv.org/abs/2508.05452 https://arxiv.org/pdf/2508.05452 https://arxiv.org/html/2508.05452
deployment of LLMs in medical domains. The dataset is released in https://github.com/llmeval/LLMEval-Med. [6/6 of https://arxiv.org/abs/2506.04078v1]
June 5, 2025 at 6:11 AM
deployment of LLMs in medical domains. The dataset is released in https://github.com/llmeval/LLMEval-Med. [6/6 of https://arxiv.org/abs/2506.04078v1]
and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective [5/6 of https://arxiv.org/abs/2506.04078v1]
June 5, 2025 at 6:11 AM
and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective [5/6 of https://arxiv.org/abs/2506.04078v1]
assessment of complex reasoning). To address these issues, we present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We [3/6 of https://arxiv.org/abs/2506.04078v1]
June 5, 2025 at 6:11 AM
assessment of complex reasoning). To address these issues, we present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We [3/6 of https://arxiv.org/abs/2506.04078v1]