Search papers, labs, and topics across Lattice.
22 papers published across 4 labs.
Automated kernel discovery now yields reusable inductive biases that generalize beyond training data, outperforming meta-learned deep kernels and even human-refined versions on unseen tasks.
Small Language Models can parse ML pipeline structures, but their insights into data science practices diverge significantly from other classifiers, highlighting the critical impact of taxonomy definition.
Current LLMs struggle to translate visual scenes into executable geometric functions, failing even basic trajectory generation tasks.
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.
Automated kernel discovery now yields reusable inductive biases that generalize beyond training data, outperforming meta-learned deep kernels and even human-refined versions on unseen tasks.
Small Language Models can parse ML pipeline structures, but their insights into data science practices diverge significantly from other classifiers, highlighting the critical impact of taxonomy definition.
Current LLMs struggle to translate visual scenes into executable geometric functions, failing even basic trajectory generation tasks.
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.
Broader tokenizer alignment in on-policy distillation can actively harm performance by introducing noisy supervision signals.
Sparse MoE models match dense models' code repair correctness with drastically fewer active parameters, challenging the sole reliance on total parameter count for evaluating code generation efficiency.
Automated code generation from 3DGS papers not
LLMs can now autonomously debug complex chip designs by translating natural language queries into SQL against massive simulation dumps.
Reasoning in base LLMs can be unlocked by simply prepending specific tokens, matching RL-tuned performance on math and coding.
GPU kernel optimization agents can regress by up to 19% when transferring distilled knowledge across DSLs, revealing that source attempt quality, not just success, dictates transfer efficacy.
Achieve state-of-the-art LLM-guided program evolution for under $2, drastically cutting costs for complex optimization tasks.
LLM-driven heuristic design now learns from noisy system logs to uncover hidden system dynamics, outperforming prior methods in real-world resource assignment.
Even the largest open-weight LLMs fail to generate accurate cybersecurity CLI commands over 58% of the time, but fine-tuning with verifiable rewards on KaliBench closes the gap with massive MoE models.
Mutation-enhanced skill evolution unlocks significant improvements in Lean theorem proving by intelligently sampling mathematical concepts when proof generation stalls.
Verifier-guided RL unlocks a 34% accuracy boost in mathematical reasoning by directly penalizing incorrect intermediate steps, not just final answers.
Coding agents can achieve over 4x throughput gains with a novel retrieval-based speculative decoding framework that intelligently sources and formats reusable text and adapts draft lengths.
Even the best AI agents fail to generate engineering-grade Simulink models, scoring below 43% on a new benchmark that rigorously tests functional correctness and robustness.
A listwise agent inspecting top candidates holistically boosts patch retrieval accuracy by over 27% compared to treating them independently.
AutoResearch systems can achieve up to 28% fewer errors by making competing behaviors visible and actively steering edits to resolve their inherent conflicts.
Lightweight sentinels can learn to proactively intervene in coding agent actions, boosting task success by up to 14% by preventing costly error propagation.
Autonomous agents can recursively self-improve LLM-generated games, achieving superhuman performance and drastically cutting generation costs by internalizing development experience.
