AI Research & Publications on AI Computing Platform | Lambda
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Publications in 2026
CVPR 2026 PixARMesh
Mesh-native autoregression for whole scenes from a single view, token-by-token.
CVPR 2026 Fractured Object Recovery
Reassembles what's left. Generates what isn't.
ICLR 2026 AgentFlow
Train the agent inside its own loop. 7B beats GPT-4o on search, math, and science.
ICLR 2026 Latent Particle World Models
Discovers objects and masks from raw video, predicts what happens next, no supervision.
ICLR 2026 Multi-Agent Social Interactions
LLMs fold under peer pressure. Our benchmark and RL recipe help smaller models hold their ground.
ICLR 2026 EdiVal-Agent
Object-level grading of multi-turn image editing, exposing where today's best editors silently break.
ICLR 2026 OffTopicEval
Give an LLM a job and clear boundaries. It still answers off-topic questions, almost every time.
ICLR 2026 Zeroth-Order Federated LLM Fine-Tuning
Sparse updates make syncing cheap enough to go frequent, neutralizing non-IID drift.
ICLR 2026 LLM Unlearning Reframed as Retrieval
Smarter data selection pushes the forget-vs-retain frontier past oracle sampling.
ICLR 2026 Principled RL for Diffusion LLMs
Token-level RL doesn't fit diffusion LLMs. Treat the whole sequence as one action, 20–40 point gains.
ICLR 2026 Align Your Structures for Molecular Dynamics
Pretrains on static molecular structures, stitches them into dynamics trajectories, bypassing simulation data scarcity.
ICLR 2026 TangoFlux
515M params, 30s of studio audio in under 4 seconds. Aligned by ranking its own outputs.
ICLR 2026 Video Native Sparse Attention
Learnable sparse attention for video. 3.6% attention budget at 128K tokens, accuracy still improves.
ICLR 2026 Exponent-Concentrated FP8
Model weight exponents cluster into 2–3 bits of entropy. Lossless FP8 compression, up to 177% faster inference.
The ARChitects secure runner-up in ARC Prize 2025
Last year, “the ARChitects,” a Lambda-sponsored team (including Lambda researcher David Hartmann) won the ARC Prize 2024. This year, they finished second out of 1,400+ teams with a final leaderboard score of 16.53%.
LLM performance benchmarks leaderboard
A clear, data-driven comparison of today's leading large language models. Standardized benchmark results cover top contenders like Meta's Llama 4 series, Alibaba's Qwen3, and the latest from DeepSeek, with critical performance metrics measuring everything from coding ability to general knowledge.
ML Times
Your go-to source for the latest in the field, curated by AI. Sift through the excess. Make every word count.
Best practices and system insights
01
Diffusion from scratch
A guide for diffusion models implemented in a single PyTorch script.
02
Text2Video pretraining
Lessons learned from training a text-to-video model with hundreds of GPUs.
03
GPU benchmarks
Throughput GPU benchmarks for training deep neural networks.
04
MLCommon benchmark
Time-to-solution benchmark for training foundation models on clusters.
Recognized by scholars and industry peers
NeurIPS 2025 Scaling laws for diffusion models
Diffusion beats autoregressive in data-constrained settings.
NeurIPS 2025 SpatialReasoner
Builds an explicit 3D scene and reasons over it step by step, boosting accuracy and generalization on 3D spatial QA benchmarks.
NeurIPS 2025 Tensor decomposition for force-field prediction
Replaces heavy tensor operations in molecular force-field models with low-rank approximation, reducing compute while keeping accuracy.
NeurIPS 2025 Bifrost-1
Aligns VLMs with diffusion models through shared CLIP patch embeddings, enabling controllable high-quality generation while preserving reasoning.
NeurIPS 2025 BLEUBERI
Using simple BLEU scores as feedback on hard instructions can train instruction-following models that rival those tuned with expensive learned rewards.
NeurIPS 2025 OverLayBench
A dataset that stress-tests layout-to-image models on heavily overlapping scenes, exposing current failures and offering an improved baseline.
Breakthroughs backed by Lambda
Bold ideas, funded and refined through the Lambda Research Grant. These are the projects shaping how AI learns, reasons, and scales — built by the researchers defining what’s next.
01
SAEBench
A comprehensive benchmark for sparse autoencoders in language model interpretability
Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Bloom, David Chanin, Yeu-Tong Lau, Eoin Farrell, Callum McDougall, Kola Ayonrinde, Demian Till, Matthew Wearden, Arthur Conmy, Samuel Marks, and Neel Nanda — ICML 2025
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02
VideoHallu
Evaluating and mitigating multi-modal hallucinations on synthetic video understanding
Zongxia Li, Xiyang Wu, Guangyao Shi, Yubin Qin, Hongyang Du, Tianyi Zhou, Dinesh Manocha, and Jordan Lee Boyd-Graber — NeurIPS 2025
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03
VLM2Vec-V2
Advancing multimodal embedding for videos, images, and visual documents
Meng, Rui and Jiang, Ziyan and Liu, Ye and Su, Mingyi and Yang, Xinyi and Fu, Yuepeng and Qin, Can and Chen, Zeyuan and Xu, Ran and Xiong, Caiming, and others — arXiv preprint 2025
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04
Think, prune, train, improve
Scaling reasoning without scaling models
Caia Costello, Simon Guo, Anna Goldie, and Azalia Mirhoseini — ICLR 2025 workshop
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05
NeoBERT
A next-generation BERT
Lola Le Breton, Quentin Fournier, Mariam El Mezouar, and Sarath Chandar — TMLR 2025
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