AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
TacticAI
Google DeepMind, in collaboration with Liverpool FC, releases TacticAI, a geometric deep-learning system that analyzes football corner kicks and suggests alternative tactics for coaches to explore.
What Are Tools Anyway? A Survey of Tool Use in LLMs
This survey establishes a formal definition of tools as "external programs used by LMs" and systematizes when, why, and how tool-use improves LLM performance.
RankPrompt: Step-by-Step Comparisons Make LLMs Better Reasoners
RankPrompt is a prompting method that lets an LLM self-rank its own candidate answers via chains of pairwise comparisons, without needing an external verifier or additional fine-tuning.
LLM4Decompile
LLM4Decompile is the first open-source family of LLMs specialized for decompiling machine code back into readable, re-executable C source.
Agent-FLAN
Agent-FLAN redesigns fine-tuning data so that open models can learn agentic skills without sacrificing general capability, hitting new open-source SoTA for Llama2-7B-based agents.
DROID
DROID is an open-source robot manipulation dataset that dramatically expands the diversity of real-world robot demonstrations available for imitation-learning research.
SIMA
DeepMind's Scalable Instructable Multiworld Agent (SIMA) is a generalist AI agent that follows natural-language instructions across nine commercial 3D video games like No Man's Sky, Teardown, Valheim, and Space Engineers.
Retrieval Augmented Thoughts (RAT)
RAT augments chain-of-thought by iteratively rewriting each reasoning step using retrieved context, sharply reducing hallucination on long-horizon generation tasks.
Knowledge Conflicts for LLMs
A survey that maps the landscape of knowledge conflicts in LLMs, covering how they arise, how models behave under them, and how to mitigate them.
Branch-Train-MiX (BTX)
Meta's BTX produces a single Mixture-of-Experts LLM by first training specialized experts in parallel and then mixing them, sidestepping the high cost of training one big generalist.
LLMs Predict Neuroscience Results (BrainBench)
BrainBench asks both LLMs and human experts to predict the outcomes of neuroscience experiments from their abstracts, and finds LLMs outperform experts.
C4AI Command-R
Cohere for AI releases Command-R, a 35B open-weight LLM tuned specifically for retrieval-augmented generation, tool use, and multilingual workflows.
MM1: Multimodal LLM Pre-training
Apple's MM1 paper runs extensive ablations on multimodal LLM pretraining choices and releases a family of models up to 30B parameters that set competitive MLLM pretraining benchmarks.
Claude 3
Anthropic releases the Claude 3 family (Haiku, Sonnet, Opus), with Opus leapfrogging GPT-4 on many standard benchmarks and bringing frontier multimodal capability plus a much larger context window.
Robust Evaluation of Reasoning
The paper introduces functional benchmarks that parameterize reasoning problems so the same structural question can be re-instantiated with fresh surface forms, then uses them to expose a large "reasoning gap" in frontier LLMs.
Can LLMs Reason and Plan?
Kambhampati's position paper argues that what looks like reasoning and planning in LLMs is better understood as "universal approximate retrieval" powered by web-scale training.
RAG for AI-Generated Content
A survey that extends RAG beyond text, showing how retrieval augmentation is being applied across code, image, audio, video, and 3D generation.
KnowAgent
KnowAgent improves LLM-based planning agents by explicitly injecting action knowledge - what the actions are and how they relate - rather than letting the LLM invent its own action space at runtime.
Sora Overview
A comprehensive academic review of OpenAI's Sora, tracing the technical ingredients behind the text-to-video "world simulator" and the opportunities/limitations for the next wave of large vision models.
Design2Code
Design2Code tackles the front-end engineering problem of turning a visual design into working HTML/CSS and gives the community both a benchmark and strong MLLM baselines.
Mistral Large
Mistral AI releases Mistral Large, its flagship closed-weight LLM positioned as the second-ranked API-accessible model behind GPT-4 at launch.
Datasets for LLMs: A Comprehensive Survey
A 180+-page survey that catalogs and analyzes the datasets that underpin modern LLM training and evaluation.
StarCoder 2
BigCode releases StarCoder 2, an open family of code LLMs at 3B, 7B, and 15B parameters trained on The Stack v2, a much larger and cleaner code corpus than the original StarCoder.
LLMs on Tabular Data: A Survey
A survey that maps how LLMs are being applied to tabular data tasks - a domain historically dominated by gradient-boosted trees and specialized architectures.