Chatbot Arena has become a pivotal leaderboard for AI systems, yet it suffers from systematic biases due to undisclosed testing practices that favor select providers, allowing them to manipulate scores by choosing the best results from multiple tests. Link to article
Chain of Recursive Thoughts: Make AI think harder by making it argue with itself
CoRT (Chain of Recursive Thoughts) enhances AI by enabling it to argue with itself, leading to significantly improved responses, particularly in programming tasks with the Mistral 3.1 24B model.
Xiaomi MiMo Reasoning Model
MiMo-7B is a groundbreaking series of models designed specifically for reasoning tasks, demonstrating superior performance in mathematics and code reasoning, even outperforming larger models like the 32B variants.
DeepSeek-Prover-V2
DeepSeek-Prover-V2 is an advanced open-source model for formal theorem proving in Lean 4, utilizing a recursive theorem proving pipeline to synthesize cold-start reasoning data from subgoal decomposition, enhancing its performance in mathematical reasoning tasks.
Everything we announced at our first LlamaCon
LlamaCon introduced the Llama API, a limited preview that merges the advantages of closed model APIs with the flexibility of open-source, enabling developers to create custom models effortlessly.
Bamba: An open-source LLM that crosses a transformer with an SSM
Bamba, IBM's new open-source LLM, merges the speed of state-space models with the expressive power of transformers, promising enhanced performance for long sequences.
An illustrated guide to automatic sparse differentiation
Automatic Sparse Differentiation (ASD) leverages the sparsity of Hessians and Jacobians to significantly enhance computational efficiency in machine learning applications, yet it remains underutilized compared to traditional automatic differentiation (AD).
Awesome List on AI for Security
Curated resource: The Awesome AI for Security repository compiles tools, papers, and datasets focused on leveraging AI technologies like Large Language Models (LLMs) for cybersecurity tasks, enhancing security operations through modern methodologies.
[R] Bringing Emotions to Recommender Systems: A Deep Dive into Empathetic Conversational Recommendation
ECR (Empathetic Conversational Recommender) enhances traditional systems by integrating user emotions into both item recommendations and dialogue generation, utilizing emotion-aware representations and feedback mechanisms.
[P] hacking on graph-grounded retrieval for SEC filings + an AI “legal pen-tester”—looking for feedback & maybe collaborators
Graph-first ingestion & retrieval aims to transform 300-page SEC filings into traceable embeddings with a target of 50 ms query latency, currently under a patent-pending pipeline development.
[Discussion] I trained a 7B LLM with only 8GB of VRAM using symbolic compression MemoryCore benchmark results
A symbolic compression pipeline enabled training a 7B parameter LLM on just 8GB of VRAM, utilizing techniques like symbolic tokenization and modular encoding layers to maintain model quality while reducing compute demands.
DYNAMAX: Dynamic computing for Transformers and Mamba based architectures
DYNAMAX introduces a novel framework that integrates early exits (EEs) into Mamba architectures, enhancing their role as efficient classifiers for both Mamba and transformer-based LLMs, thus addressing a gap in current research.
Reinforcement Learning for LLM Reasoning Under Memory Constraints
Reinforcement learning (RL) techniques enhance reasoning in large language models (LLMs) under memory constraints, introducing S-GRPO and T-SPMO for improved performance on benchmarks like SVAMP.
Hallucination by Code Generation LLMs: Taxonomy, Benchmarks, Mitigation, and Challenges
LLMs can generate source code but are susceptible to hallucinations, which are incorrect outputs that can be difficult to detect and rectify, especially in complex codebases.
Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?
LLMs exhibit significant variability in detecting intrinsic hallucinations during paraphrasing and machine translation, influenced by factors such as model size and prompt choice, as evaluated through the HalluciGen task.