Search papers, labs, and topics across Lattice.
54 papers published across 7 labs.
Coding agents routinely fail to build agents that truly meet user requirements, with the gap between demonstration and delivery being the norm.
Real-world professional tasks demand agents that learn from exploration and feedback, a capability unlocked by a novel engine that reconstructs environments to force agents through the full problem-solving lifecycle.
Human GUI interactions can be precisely captured and translated into reusable agent skills, dramatically boosting performance on unseen software and workflows.
Predictive world models, not just static feedback, unlock substantial gains in recursive self-improvement across diverse domains, from code to scientific discovery.
Existing 3D asset generators can now produce objects that not only look good but also move and function realistically, passing physics tests for stability and collision-free articulation.
Coding agents routinely fail to build agents that truly meet user requirements, with the gap between demonstration and delivery being the norm.
Real-world professional tasks demand agents that learn from exploration and feedback, a capability unlocked by a novel engine that reconstructs environments to force agents through the full problem-solving lifecycle.
Human GUI interactions can be precisely captured and translated into reusable agent skills, dramatically boosting performance on unseen software and workflows.
Predictive world models, not just static feedback, unlock substantial gains in recursive self-improvement across diverse domains, from code to scientific discovery.
Existing 3D asset generators can now produce objects that not only look good but also move and function realistically, passing physics tests for stability and collision-free articulation.
Frozen VLAs can recover from unseen actuator faults by inferring joint capabilities online, boosting success rates from 24.8% to 59.3% on 28 tasks.
Frozen LLMs can autonomously achieve recursive self-improvement by learning and refining explicit, executable world models from interaction data.
LLM agents significantly outperform numerical optimizers in four out of five black-box optimization domains, but adding more numerical tools doesn't always help.
Pretrained LLMs can drive complex robot navigation tasks across diverse environments and requests without any navigation-specific fine-tuning, by offloading motion execution to specialized tools.
A single visual world model can now execute complex procedural tasks end-to-end, adapting its generation to the actual outcome of each predicted action.
Spatial coding agents can be distilled into standalone MLLMs, achieving superior spatial reasoning and generalization without external tools.
LEGO-building AI agents can assemble physically valid structures from text prompts, but their designs lag significantly behind human creativity and complexity.
LLM agents can now be monitored and intervened upon in real-time, saving 18% of compute by detecting failures within the first 8 steps with 83% accuracy.
Visual-native skill cards, directly accessible to VLMs, unlock a self-reinforcing loop of policy improvement and skill accumulation, drastically boosting sample efficiency.
Ambiguous object selection in text-guided image editing is solved by directly annotating the canvas, achieving state-of-the-art performance in complex edits.
LLMs can now autonomously learn to self-improve at test-time, nearly doubling utility by intelligently selecting and applying TTT strategies.
Manual assembly instruction writing is now obsolete: LLMs can generate accurate, CAD-grounded procedures, preserving engineering details and passing automated checks.
A frozen video tokenizer's latent can be factored into controllable and reward-relevant components, unlocking significant downstream planning gains.
Casual images are now enough to generate high-fidelity, scene-aligned 3D objects with accurate pose, even from sparse observations.
Human motion alone, when translated into a shared latent space, unlocks zero-shot generalist humanoid control for locomotion and manipulation tasks.
Research reports can now be continuously updated with over 60% fewer search calls and 33% less token consumption, maintaining high quality over time.
LLMs can now generate executable feature programs from long text, enabling efficient structured data prediction without real-time LLM inference.
LLMs adapt neural operator configurations using both a strong task-dependent prior and real-time experimental feedback, outperforming Bayesian optimization.
A residual memory mechanism allows LLMs to retain crucial context for long-horizon tasks, achieving near full-context performance with only 5.2% of input positions.
LLMs can be steered to reliably interpret complex genomic variants by grounding their reasoning in deterministic biological computations and evidence verification, avoiding hallucinated TF binding changes and fabricated experimental support.
Agents learning to optimize local rewards in spatial public goods games can inadvertently create detrimental spatial patterns that reduce collective welfare by over 40% compared to random movement.
Collaboration among AI agents can trigger a population explosion and runaway cyber capability, even if individual agents are weak, by creating a critical population threshold for takeoff.
Most AI agent skills copied on GitHub originate from a few hidden hubs, rendering security patches ineffective for the vast majority of users.
Fine-grained embodied interactions between multiple agents can be effectively modeled by synchronizing ego-stream generation with cross-view action consistency and shared environment memory.
LLM agents can achieve high task accuracy while exhibiting profound epistemic *im*humility, failing to acknowledge uncertainty even when presented with contradictory evidence.
A unified multimodal agent, OneSearch-VL, achieves state-of-the-art performance on diverse visual research tasks by explicitly modeling and preserving interdependencies within a Visually Grounded Evidence Graph.
Coding agent benchmarks can now dynamically scale difficulty and avoid data leakage by having agents evaluate each other, with the latest models generating tasks that defeat older solvers 10x more often.
Agents can learn explicit, reusable mental models of partners to dramatically improve multi-agent interaction quality and Theory-of-Mind capabilities.
AI agents can learn to climb leaderboards in complex adversarial games by refining executable policies, but struggle with intricate rule sets
Synthesized software environments unlock a 50% boost in CUA performance on unseen benchmarks, proving scalable supervision is key.
LLM agents can learn from past mistakes by retroactively revising their internal "harness" state, significantly improving continual learning performance.
Amortized agentic policy discovery enables personalized test-time scaling to satisfy complex, multidimensional user requirements, outperforming single-objective optimization on unseen profiles.
A novel contrastive feedback mechanism allows LLM agents to learn skill proposals that are aligned with the current actor's behavior without requiring expensive skill evaluations.
LLM agents can dynamically evolve their skill libraries, preventing obsolescence and improving long-horizon task performance by up to 7.8% through a fitness-driven lifecycle.
LLM agents can now reliably track and fulfill complex deliverable requirements across numerous files without losing context, thanks to an environment-side "tab" that acts as a persistent, shared task ledger.
AI agents are now building climate models that outperform published research and offer novel hypotheses for long-standing scientific debates.
An LLM-based agent achieves 18.3% out-of-sample returns in stock options trading by learning to select structured strategies, but surprisingly, retaining news during RL training halves performance.
Frontier LLMs can achieve substantial recursive self-improvement, closing nearly half of the performance gap on complex benchmarks by jointly optimizing data, training, and execution harnesses within a unified, agent-native environment.
Uncoded crash factors like phone use and medical episodes are vastly undercounted in police records, with narratives revealing 2x more injury crashes than coded fields.
A frozen graph backbone effectively preserves past structural knowledge in continual multi-agent reinforcement learning, dramatically reducing forgetting and boosting transfer across tasks with distinct graph structures.
Planning in visual world models can be accelerated by $5\times$ by pruning $50\%$ of spatial tokens based on their influence on the planning cost gradient, without sacrificing performance on most control tasks.
AI is poised to revolutionize ARPES, moving beyond data analysis to enable fully autonomous, self-driving experimental labs.
Intermediate retrieval signals, not just final outcomes, unlock efficient credit assignment for training LLM search agents.
AI agents can now build and reuse persistent, artifact-specific knowledge, dramatically improving their ability to find relevant information for complex tasks and reducing operational costs.
LLMs can learn to actively compute evidence by routing through retrieval and computation, dramatically improving performance on complex, multi-source reasoning tasks.
Physical robot learning can now autonomously discover and exploit informative experiments to continuously improve foundation models, achieving 77% success on RoboCasa365 and zero-shot transfer to real hardware.
Frontier multimodal agents struggle to autonomously acquire and utilize task-relevant information in unfamiliar physical environments, failing 77% of the time on a new exploration benchmark.
Key contribution not extracted.
Real-world coding agent usage reveals that task flow, not just task completion, dramatically impacts agent cost and effectiveness, suggesting current benchmarks are misaligned with user behavior.
