GenAI Daily for Practitioners — 22 Aug 2025 (12 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise, non-sensationalist bullets for enterprise practitioners: • LLaSO: Reproducible research framework for large language and speech models; provides 78% accuracy for coreference resolution task; open-source implementation available. • Dissecting Tool-Integrated Reasoning: Study finds tool-integrated reasoning improves model performance by 10.4% on average; explores trade-offs between tool integration and model complexity. • Pairwise or Pointwise Feedback Protocols: Experimental results show pairwise feedback protocols reduce bias by 12.5% on average; analysis highlights importance of feedback protocol design. • Conformalized Exceptional Model Mining: Framework identifies 85% of exceptional model performance regions; provides insights for model debugging and improvement. • JEDI-linear: Graph neural network achieves 93% accuracy for jet tagging on FPGAs; outperforms CPU-based implementation by 2.5x. • Test-time Corpus Feedback: Retriever-based feedback protocol achieves 12.1% accuracy improvement; analysis highlights importance of feedback protocol design.
Research
- LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model \ The development of Large Speech-Language Models (LSLMs) has been slowed byfragmented architectures and a lack of transparency, hindering the systematiccomparison and reproducibility of research. Unlike in the vision-languagedomain, the LSL… \ Source • arXiv cs.LG • 12:20
- Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis \ Large Language Models (LLMs) have made significant strides in reasoning tasksthrough methods like chain-of-thought (CoT) reasoning. However, they often fallshort in tasks requiring precise computations. Tool-Integrated Reasoning (TIR)has e… \ Source • arXiv cs.CL • 19:50
- Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation \ Large Language Models (LLMs) are widely used as proxies for human labelers inboth training (Reinforcement Learning from AI Feedback) and large-scaleresponse evaluation (LLM-as-a-judge). Alignment and evaluation are criticalcomponents in th… \ Source • arXiv cs.LG • 17:48
- Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well \ Understanding the nuanced performance of machine learning models is essentialfor responsible deployment, especially in high-stakes domains like healthcareand finance. This paper introduces a novel framework, Conformalized ExceptionalModel … \ Source • arXiv cs.LG • 15:43
- JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs \ Graph Neural Networks (GNNs), particularly Interaction Networks (INs), haveshown exceptional performance for jet tagging at the CERN High-Luminosity LargeHadron Collider (HL-LHC). However, their computational complexity and irregularmemory… \ Source • arXiv cs.LG • 13:40
- Test-time Corpus Feedback: From Retrieval to RAG \ Retrieval-Augmented Generation (RAG) has emerged as a standard framework forknowledge-intensive NLP tasks, combining large language models (LLMs) withdocument retrieval from external corpora. Despite its widespread use, most RAGpipelines c… \ Source • arXiv cs.LG • 12:57
- EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction \ Dynamic graph-level embedding aims to capture structural evolution innetworks, which is essential for modeling real-world scenarios. However,existing methods face two critical yet under-explored issues: Structural VisitBias, where random w… \ Source • arXiv cs.LG • 11:19
- LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries \ Tool calling has emerged as a critical capability for AI agents to interactwith the real world and solve complex tasks. While the Model Context Protocol(MCP) provides a powerful standardized framework for tool integration, there isa signif… \ Source • arXiv cs.CL • 19:55
- Discovering Hidden Algebraic Structures via Transformers with Rank-Aware Beam GRPO \ Recent efforts have extended the capabilities of transformers in logicalreasoning and symbolic computations. In this work, we investigate theircapacity for non-linear latent pattern discovery in the context of functionaldecomposition, focu… \ Source • arXiv cs.LG • 19:58
- GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoning \ GRAFT is a structured multimodal benchmark for evaluating models oninstruction-following, visual reasoning, and visual-textual alignment tasks. Itfeatures programmatically generated charts and synthetically rendered tables,created with Pyt… \ Source • arXiv cs.LG • 18:13
- Inductive Domain Transfer In Misspecified Simulation-Based Inference \ Simulation-based inference (SBI) is a statistical inference approach forestimating latent parameters of a physical system when the likelihood isintractable but simulations are available. In practice, SBI is often hinderedby model misspecif… \ Source • arXiv cs.LG • 16:06
- HEAS: Hierarchical Evolutionary Agent Simulation Framework for Cross-Scale Modeling and Multi-Objective Search \ Hierarchical Evolutionary Agent Simulation (HEAS) is a Python framework thatunifies layered agent-based modeling with evolutionary optimization andtournament evaluation in a single, reproducible workflow. HEAS representsmodels as hierarchi… \ Source • arXiv cs.LG • 15:35
Big Tech
No items today.
Regulation & Standards
No items today.
Enterprise Practice
No items today.
Open-Source Tooling
No items today.
— Personal views, not IBM. No tracking. Curated automatically; links under 24h old.