Alex Smola

AI on Review: How Large Language Models Are Reshaping Peer Review The Peer Review Crunch and New "Reviewer Duties" Peer review is the backbone of scientific quality control, ensuring that research findings are vetted for accuracy and significance before publication (pmc.ncbi.nlm.nih.gov). In fast-moving fields like machine learning and computer vision, top conferences function much like journals - and the integrity of science depends on rigorous peer evaluation. However, the system is straining under an avalanche of submissions. Major AI conferences now routinely receive well over 10,000 papers, a surge that has stretched the reviewer pool to its limits (arxiv.org). This deluge has led to radical policy changes: some conferences now essentially conscript all submitting authors into service as reviewers. For example, ICLR 2025 explicitly warned authors that any paper without at least one author signed up to review would be desk-rejected (reddit.com). NeurIPS and others have similarly pleaded that "all authors help with reviewing, if asked," to tackle the reviewer shortage. https://lnkd.in/d7KDP99K

24

New paper on improving LLMs' native capabilities of retrieval/ranking, to be presented in NeurIPS25: https://lnkd.in/eSX8vKPm Can we give LLM a list of items in context and ask it to retrieve/rank relevant ones? How does LLM solve such a problem? Is attention doing something similar to embedding-based retrieval? How to make it efficient with long context? Can we leverage embedding learning techniques to improve quality?

301

5 Comments

New research presented at NeurIPS 2025 explores how to build fairer and more sustainable data ecosystems for large language models. The study, coauthored by AIMSEC Faculty member Beibei L. of Carnegie Mellon’s Carnegie Mellon University - Heinz College of Information Systems and Public Policy, proposes a framework for LLM data markets that evaluates dataset contributions and introduces a “fairshare” pricing mechanism, highlighting how transparent and equitable pricing could strengthen training data markets while improving model performance and long-term data supply. You can read more here! https://lnkd.in/erSeijPt

1

🚀 Smarter, Faster RAG: A Policy-Optimized, Memory-Efficient Framework for Test-Time Reasoning 📄 New arXiv print 🔗 Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding https://lnkd.in/dErdTDf7 🔍 The Problem Retrieval-Augmented Generation (RAG) boosts LLMs with external knowledge but suffers from: ❌ Static retrieval schedules ❌ Distractor-induced hallucinations ❌ Linear memory growth in KV caching ❌ Inefficient test-time compute without reward alignment. 🧠 Our Framework: PORAG + ATLAS + CRITIC We propose a three-component architecture that dynamically optimizes retrieval, rewards factual and high-quality outputs, and reduces compute/memory costs — all without altering the pretrained base model at inference. 1️⃣ PORAG: Policy-Optimized Retrieval-Augmented Generation • Built on Group Relative Policy Optimization (GRPO) • Fine-tunes with QLoRA adapters, guided by two specialized reward heads: – Retrieval Fidelity (factual correctness) – Response Quality (fluency, coherence, utility) • Enables single-shot decoding with KL-regularized policy updates for stability 2️⃣ ATLAS: Adaptive Token-Layer Attention Scoring • Detects when to retrieve via Multi-Layer Attention Gradient (MLAG) • Builds what to retrieve via Layerwise Representation Pooling (LRP) • Leverages hybrid attention-embedding relevance, with entropy-aware scaling to manage retrieval load 3️⃣ CRITIC: Cache Reduction via Importance-based Token Inclusion Criteria • Selectively retains high-impact tokens in KV cache • Hybrid importance signal: attention strength, entropy spread, gradient sensitivity • Reduces memory and latency for long-context generation, with adaptive compression ⚙️ Exhaustive Test-Time Inference Scaling — No Fine-Tuning Required Our framework integrates advanced inference-time scaling techniques that allow LLMs to trade off compute, latency, and answer quality — dynamically and intelligently. These include: ✔️ Entropy-Guided Decoding — invoke deeper reasoning under uncertainty ✔️ Monte Carlo Tree Search (MCTS) — strategic decoding via multi-branch simulation ✔️ Mixture of Agents — task-routed decoding using a pool of specialized policies ✔️ Lookahead Decoding — speculative multi-token generation and verification These methods are plugged into our test-time control module, which uses token-level entropy, compute budget, and reward models to dynamically select the optimal strategy for each input. 🧠 Conclusion Our framework enables scalable, retrieval-aware LLM inference by combining reward-optimized generation, introspective retrieval, and memory-efficient decoding. It is: ✔️ Transformer-agnostic ✔️ Plug-and-play for SLMs ✔️ Ready for enterprise-scale, compute-sensitive deployments 📌 Full paper on arXiv: https://lnkd.in/dErdTDf7 🤝 With Akash Das, SHIVAM GUPTA, and Venkataramana Runkana #LLMInference #TestTimeScaling

17

The "titans" have spoken. From Terence Tao to Donald Knuth, the world’s leading minds are moving past the AI hype and into deep, intentional utility. As we apply these LLMs to healthcare discoveries, the goal isn't just speed—it’s the technical rigor that Tao and Knuth are demonstrating. Moving from "generative" to "verifiable" is the next frontier for AI in the life sciences! #GenAI #MachineLearning #ArtificialIntelligence #LLMs #AIHealthcare #LifeSciences #Biotech

3

We just open-sourced FAI-RL, Roblox's reinforcement learning library built for scalable LLM post-training. If you’re working on RLHF, reasoning models, or policy optimization, FAI-RL makes it easy to experiment with DPO, PPO, GRPO, and GSPO, and integrates seamlessly with Hugging Face for quick iteration and prototyping. Looking forward to hearing your thoughts and feedback!

17

1 Comment