Search papers, labs, and topics across Lattice.
STRACE transforms noisy execution traces into precise optimization signals, leading to a 42.5% to 58.5% success rate improvement in agent performance.
ReOPD turns the costly process of agent-environment interaction into a reusable offline resource, achieving faster training while preserving accuracy.
Despite high diagnostic accuracy, LLMs fail to choose valid recovery actions for over 60% of incidents, exposing a critical flaw in their operational utility.
By treating slide design as an inverse planning problem, SPIRE reveals latent design intents that traditional methods miss, leading to superior personalization outcomes.
Despite the promise of advanced AI agents, even the best performers struggle, with Codex GPT-5.5 only achieving a 42% success rate in complex healthcare tasks.
Mandol achieves a 5.4x speedup in retrieval and a 4.8x speedup in insertion, revolutionizing long-term conversational memory management.
Bridging the Context Gap in T2I models, Qwen-Image-Agent achieves state-of-the-art performance by intelligently constructing context from user input and external sources.
Reusable procedural skills derived from agent traces can drastically cut down execution time and boost success rates in complex tasks.
G2PO redefines agent actions and leverages a global state-transition graph, leading to a 22.2% boost in success rates for long-horizon tasks.
Agentic AI could revolutionize cybersecurity by transforming labor-intensive tasks into efficient, automated defenses.
CoTriSyGen achieves unprecedented long-range coherence in video generation by integrating visual evidence into a dynamic memory system, drastically reducing identity drift across shots.
FastContext cuts coding agent token usage by 60% while boosting resolution rates by 5.5% by decoupling code exploration from task-solving.
Current AI agents excel in structured tasks but falter at generating novel insights and tackling open-ended scientific challenges.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
Current models struggle with hybrid interface tasks, achieving only a 41.2% success rate, underscoring a critical gap in CUA evaluation.
Selective context pruning combined with automated summarization can boost LLM performance to over 91% in complex enterprise tasks while slashing token usage and processing time.
MAGE redefines memory management for long-horizon agents, achieving up to 20.4% higher task success rates while slashing token usage by over half.
Agent Development Kits vary dramatically in usability, with some enabling agents to outperform general-purpose coding tools at a fraction of the cost.
RHO transforms how AI agents can autonomously refine their skill sets without requiring labeled data, achieving a remarkable 19% increase in performance in just one optimization round.
AsyncWebRL achieves a staggering 2.9× increase in training throughput while setting a new state-of-the-art performance for web agents on challenging tasks.
OpenWebRL-4B sets a new benchmark for open-source visual web agents, achieving impressive success rates with minimal initial data while outperforming larger-scale competitors.
SeClaw reveals that existing benchmarks fall short in capturing the complexities of agent behavior, enabling a more nuanced evaluation of security risks in autonomous systems.
Stop rebuilding your entire MCP server every time your API spec changes; DeltaMCP regenerates only what's needed.
Stop hand-tuning your retrieval pipelines: BRANE slashes costs by up to 89% while matching accuracy by dynamically configuring pipelines per query.
Turns out, the terminal feedback your CLI agent throws away is actually a goldmine of dense supervision, allowing for significant performance gains and even self-improvement.
Model-generated skills can actually hurt agent performance, and bigger models don't necessarily make for better skill extractors or consumers.
SkillOpt transforms agent skill development into a reproducible optimization process, achieving state-of-the-art results by treating skills as trainable parameters.
AI-driven scientific discovery is closer than you think, but current systems still struggle with reproducibility, cross-domain robustness, and accountable scientific closure.
Knowing which component to tweak is half the battle: directly evaluating harness optimizers via priority ranking reveals whether they're making informed decisions or just stumbling upon improvements.
Forget human-readable models: Agentic-imodels evolves ML models that are optimized for LLM interpretability, boosting agentic data science performance by up to 73%.
Users who actively participate in an AI agent's spreadsheet execution not only improve task outcomes, but also gain a deeper understanding and feel more ownership over the results.
LLMs are poised to flip the script on personalization, giving users unprecedented control over their data and how it's used across platforms.
A groundbreaking framework reduces false positives in recommendation systems by over 74%, restoring user control and transparency in content curation.
Imagine software that autonomously evolves and maintains itself – this paper lays out the architectural groundwork for making that a reality.
Iterative visual refinement lets agents navigate dense coding IDEs with superhuman precision, outperforming single-shot methods and paving the way for more reliable software engineering agents.
Autonomous web agents get a serious upgrade with WebXSkill, which lets them learn and execute skills with both code-level precision and human-readable guidance.
Don't let your SWE agent drown in context: SWE-AGILE maintains performance on multi-turn software engineering tasks by dynamically managing reasoning context with a novel sliding window and compressed reasoning digests.
Developers want AI to handle the grunt work around coding, but hands off when it comes to the creative core – revealing that the true value of AI tooling may lie in knowing where *not* to help.
Gaze-tracking unlocks a new level of personalized AI assistance, enabling LLMs to infer user cognitive states and boost recall performance.
Knowing the *perfect* API to use or *exact* location to edit could drastically improve SWE agent performance, but knowing the perfect regression test result? Not so much.
Process-level evaluation reveals hidden skill discrepancies among web agents, enabling targeted improvements that traditional success metrics overlook.
GeoAI assistants remain unproductive because they lack a crucial agency layer for iterative human-AI collaboration, a gap this paper addresses with nine core primitives.
Generative multi-agent systems spontaneously exhibit collusion and conformity, mirroring societal pathologies, even without explicit programming and bypassing individual agent safeguards.
LLM agents can slash task completion time by almost 50% simply by predicting and pre-executing likely tool calls.
AI-generated code's fluency masks a critical flaw: it often fails to deliver what users actually intend, highlighting the urgent need for "intent formalization" to bridge the gap between informal requirements and precise program behavior.
LLM agents can learn to explore novel states and generalize to new tasks with a hybrid on- and off-policy RL framework that leverages memory.
GUI agents can achieve significantly stronger task-solving capabilities through carefully designed post-training and data curation, without relying on costly online data collection.
AgentOS reimagines LLMs as reasoning kernels within a structured OS, offering a blueprint for more robust and scalable AI agents.
Forget slow, reactive GUI agents – ActionEngine uses a state-machine memory to plan actions programmatically, slashing costs by 11.8x and doubling speed while boosting task success to 95%.
Imagine a world where web agents don't just click and type, but orchestrate complex tasks with the reliability of APIs – Web Verbs offer a path to that future.
