Search papers, labs, and topics across Lattice.
100 papers published across 13 labs.
Ultra-long video grounding is solved by evolving reusable agent skills and tools from VLM reasoning, bypassing parameter updates and separate planning models.
LLM reasoning can be significantly improved by penalizing token probabilities that drift under semifactual interventions, even without model retraining.
LLMs can learn to generate syntactically valid text without sacrificing semantic quality, even with underspecified prompts, by leveraging self-supervised rewards derived from their own likelihoods.
Humans make better decisions when AI selectively reveals information, but only if the AI learns *when* to reveal it, outperforming both full disclosure and no disclosure.
A single time-series language model now masters forecasting, contextual prediction, and temporal reasoning, outperforming specialized models on key benchmarks.
Ultra-long video grounding is solved by evolving reusable agent skills and tools from VLM reasoning, bypassing parameter updates and separate planning models.
LLM reasoning can be significantly improved by penalizing token probabilities that drift under semifactual interventions, even without model retraining.
LLMs can learn to generate syntactically valid text without sacrificing semantic quality, even with underspecified prompts, by leveraging self-supervised rewards derived from their own likelihoods.
Humans make better decisions when AI selectively reveals information, but only if the AI learns *when* to reveal it, outperforming both full disclosure and no disclosure.
A single time-series language model now masters forecasting, contextual prediction, and temporal reasoning, outperforming specialized models on key benchmarks.
Trusting LLM accuracy gains from sampling multiple answers ($k$) requires vastly more samples than commonly assumed; this work cuts certification costs by up to 47% by accounting for inter-question variance.
Recursive reasoning models achieve state-of-the-art algorithmic performance and OOD generalization with a simple optimization recipe, decoupling architectural complexity from recurrent state stabilization.
Chemists can now precisely steer retrosynthesis to favor specific precursor sets, moving beyond simple reaction recovery to nuanced chemical design.
A single learned linear layer, not the shared Transformer weights, dictates distinct computations within each recurrent loop of a Looped Transformer, with attention patterns causally routing these operations.
Quantized reasoning models overthink more, negating efficiency gains, but a new method precisely targets and penalizes these "overthinking" tokens without retraining.
Most ALMs fail to ground their audio deepfake detection rationales in actual acoustic evidence, despite generating plausible explanations.
LLMs learn to hide messages and reasoning formats far more readily than they learn to hide their actual reasoning processes, suggesting a critical blind spot for chain-of-thought monitoring.
Exploiting local parameter perturbations of a teacher model unlocks complementary supervision signals, boosting LLM mathematical reasoning accuracy beyond standard on-policy self-distillation.
LLM performance on new tasks can be boosted by intelligently reusing past computations, even when tasks are independent.
Learning protein folding's spatial logic unlocks significant gains in LLM reasoning across diverse domains, not just structure prediction.
AI-generated scientific papers exhibit a distinct "scientific slop" in global reasoning, detectable with 85.9% accuracy and impacting publication success, but can be effectively mitigated by grounding revisions in experimental evidence.
LLM agents can now adapt their action spaces dynamically from natural language, bypassing the need for pre-defined finite fields and accelerating inference through compositional policies.
VLMs struggle to assess physical plausibility, failing to bridge visual recognition with genuine physical understanding even when perception and reasoning are intact.
Clinical LLM agents can now autonomously gather evidence from longitudinal medical records and imaging, achieving superior accuracy to GPT-5.4 and Claude Opus.
Quantum learning algorithms can achieve cubic separations over classical randomized learners, shattering the longstanding belief that exact learning speedups are constrained by Grover search and Bernstein-Vazirani limits.
Standard graph reconstruction benchmarks hide massive hallucinations in plain sight: language models can simultaneously invent and lose dozens of edges while registering near-zero aggregate distortion, a blind spot resolved by converting spectral distance bounds into an explicit certificate of mixed editing.
Treating every teacher token equally during on-policy distillation leaves massive gains on the table—dynamically reweighting supervision allows a smaller student model to outperform its own larger teacher on complex reasoning tasks.
Causal generative models can now recover true joint distributions and counterfactual queries even when not a single complete record exists in the dataset.
Heterogeneous LLMs can fix each other's dead-end zero-reward rollouts in RLVR through off-policy trajectory swapping, boosting math reasoning by up to 4.5 points without extra compute or a stronger teacher model.
Chain-of-thought token bloat can be slashed by 27% while actually boosting accuracy simply by recognizing that reasoning capacity lives almost entirely within the null space of a base model's dominant singular vectors.
Collapsing pre-, mid-, and post-training into a single fully synthetic run matches open-weight model performance while slashing required training tokens by up to 140×.
Exposing the critic to both successful and failed same-prompt rollouts as contrastive foils resolves PPO's notoriously brittle credit assignment in multi-step reasoning—retaining its efficacy even when paired with a downscaled critic.
Sparse neuron dictionaries are not just passive interpretability tools: actively steering frozen VLM tokens through query-guided concept activation delivers up to a +9.5 point accuracy boost on complex spatial reasoning without retraining.
Swapping deep networks for symbolic logic rules specifically at taxonomic leaf nodes delivers verifiable, human-readable explanations across hundreds of granular classes without sacrificing hierarchical context.
Long-horizon agents plateau under naive inference scaling unless compute is explicitly redirected from task workers to a dedicated meta-reasoning controller that manages execution flow.
Up to 31.6% of correct model solutions rely on semantically invalid reasoning traces that look deceptively sound, revealing that chain-of-thought generation frequently acts as an unfaithful facade rather than a true computation graph.
A novel inference-time sampling strategy unlocks frontier LLM reasoning capabilities in smaller models by resolving the exploration-exploitation dilemma.
Large-scale rewards can unfairly suppress learning signals from smaller rewards in multi-objective RL; CorrGRPO fixes this by normalizing reward covariances.
Treating on-policy distillation as value-based RL slashes rollout compute by 75% while actively preserving the generation diversity needed for high-k reasoning.
Extra test-time "thinking" cannot recover missing parametric facts, but teaching small reasoning models to recognize their own knowledge boundaries and selectively query larger models lets a 4B model outperform a 14B baseline at nearly a third of the serving cost.
Static problem pools cause LLM reasoning post-training to stall as models saturate the dataset, but dynamically synthesizing frontier tasks via regret-guided procedural generation keeps learning signals saturated for sustained gains.
Accurate latent reconstruction is a red herring for diffusion-based reasoning; asynchronous denoising over multi-layer teacher representations allows a compact 638M model to solve complex math and code without autoregressive rollouts.
Standard next-token prediction on an agent's self-generated explanations outperforms GRPO on SWE-bench in half the training updates—even bootstrapping on tasks where every initial rollout failed.
Massive 100K-node routing problems can be solved in tens of seconds without learned heuristics simply by optimizing a compressed surrogate space to seed downstream solvers.
LLMs fail at chemical planning not from deficient reasoning capacity, but from an acute representation alignment bottleneck that disappears when structured through intermediate molecule and reaction abstractions.
Multi-agent collaboration often performs worse than solo reasoning when peers hallucinate, but Bayesian tracking of advisor reliability rooted in internal belief states keeps collective performance strictly above autonomous baselines even under high misinformation.
Grounding policy optimization in canonical disease graphs allows a compact 9B model to outperform GPT-5.5 on long-tail clinical diagnosis, revealing that controlled evidence routing matters far more than raw retrieval volume.
While Chain-of-Thought models overfit to local heuristics and collapse on longer reasoning chains, latent reasoning architectures autonomously discover a sparse, recurrent forward-search circuit that generalizes to unseen depths.
Even the strongest LLM agent workflows top out at just 25.4% proof coverage on STOC and COLT theorems, exposing a massive capability overhang between competition math performance and genuine theoretical computer science research.
Multi-agent collaboration bottlenecks when agent personas stay rigid, but dynamic, test-time strategy transplantation across system prompts enables heterogeneous LLMs to collectively align on the most effective reasoning path.
Where black-box models fall short on legal auditability, coupling SPARQL-based symbolic reasoning with decision-tree query refinement yields deterministic, provably explainable regulatory compliance decisions for law enforcement agencies.
Clinical diagnosis does not require training labels: mapping patient observations directly into structured medical knowledge graphs yields up to 0.996 F1 while remaining completely auditable.
Key contribution not extracted.
External agent scaffolding can be discarded after training by distilling evolved decision-making procedures directly into a model's native reasoning traces.
Extended reasoning modes paradoxically degrade an LLM's ability to assess evidence sufficiency in dynamic diagnosis, driving models to prematurely lock into diagnoses that succeed only by chance.
Step-level PRM guidance in discrete diffusion models actually degrades reasoning performance by up to 13 percentage points compared to simple best-of-N sampling, tripped up by noisy early-step rewards that aggressively prune viable solutions.
Unified models can master multi-turn visual self-correction through end-to-end trajectory RL, eliminating the need for external critic models or inference-time verifiers while yielding double-digit visual alignment gains.
Uniform token-level distillation often punishes valid student reasoning, but dynamically reallocating teacher supervision using verified outcome agreement unlocks immediate gains across math and code tasks without relying on hard trajectory filtering.
Frontier LLMs fail to diagnose co-occurring medical conditions in over 90% of interactive consultations because they suffer from single-hypothesis fixation—anchoring on initial findings and failing to explore parallel diagnostic branches.
Jailbreaking reasoning models turns out to be an effective antidote to RL-induced overconfidence, recovering suppressed pre-alignment entropy to slash black-box calibration error by up to 5×.
Spatial inconsistency in VLMs stems from a dissociable role-binding circuit that can be linearly steered to fix swapped-object reasoning failures on natural images without fine-tuning.
