Search papers, labs, and topics across Lattice.
100 papers published across 10 labs.
Decision-oriented LLMs like Jev offer a novel quality-latency operating regime for recommendation reranking, outperforming pointwise LLMs in scaling while maintaining strong effectiveness.
Self-evolving AI agents can become blind to their own errors, with internal rewards masking a steady decline in factual accuracy, but a novel cross-training method forces them to confront their mistakes.
State-of-the-art VLMs struggle to execute scientific research workflows, revealing a critical gap in their ability to interact with specialized software and produce verifiable results.
Faster computer-use agents are bottlenecked by their speed, but existing benchmarks are irreproducible; this new standardized framework reveals counterintuitive speed-efficiency trade-offs and that current open-weight models lag behind.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.
Decision-oriented LLMs like Jev offer a novel quality-latency operating regime for recommendation reranking, outperforming pointwise LLMs in scaling while maintaining strong effectiveness.
Self-evolving AI agents can become blind to their own errors, with internal rewards masking a steady decline in factual accuracy, but a novel cross-training method forces them to confront their mistakes.
State-of-the-art VLMs struggle to execute scientific research workflows, revealing a critical gap in their ability to interact with specialized software and produce verifiable results.
Faster computer-use agents are bottlenecked by their speed, but existing benchmarks are irreproducible; this new standardized framework reveals counterintuitive speed-efficiency trade-offs and that current open-weight models lag behind.
LLM agents can improve their decision-making by learning from their own diagnosed failures, with first-proposal corrections boosting success rates by over 30 percentage points.
Direct-decision models exhibit a learned "ordinal-scale-utilization bias," compressing the effective decision space and underutilizing available scale points, even when accuracy is high.
Clinical MLLMs can be significantly improved by optimizing for AUROC via pairwise instance rankings, reversing accuracy-based prompt evolution's performance degradation.
Distilling real GPT-4o-labeled reports, not synthetic data, enables open-weight models to match proprietary LLMs for high-value clinical entity extraction.
Standard time-series forecasting losses systematically fail on pathological demand regimes, creating a bias irreducible by model capacity alone.
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.
A single graph edge misspecification can transform a seemingly robust causal action verifier into a dangerous liability, executing harmful actions with internally valid but misleading certificates.
Source-validation accuracy is a poor proxy for out-of-distribution reliability; re-ranking checkpoints by aggregated source reliability metrics significantly boosts target probability quality without needing target data.
AutoResearch systems can achieve up to 28% fewer errors by making competing behaviors visible and actively steering edits to resolve their inherent conflicts.
A single model's representation can predict how it disagrees with others, unlocking efficient input reliability estimation without needing multiple models at inference.
Chemists can now precisely steer retrosynthesis to favor specific precursor sets, moving beyond simple reaction recovery to nuanced chemical design.
Most ALMs fail to ground their audio deepfake detection rationales in actual acoustic evidence, despite generating plausible explanations.
Most LLM lie detectors fail when models adopt anti-factual personas, instead latching onto spurious correlations like instruction following.
Existing SF-UniDA methods fail catastrophically on time series due to a highly sensitive inference threshold, but a novel auto-thresholding module offers a universal fix.
Low-precision matrix multiplication, even when repaired for accuracy, can break LLM prefix invariance and alter downstream task performance, necessitating a new certification approach.
Frontier LLMs can be trained to selectively ignore unreliable external guidance, a capability crucial for robust agentic behavior beyond simple task accuracy.
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.
Legal LLMs can now be optimized for nuanced quality beyond correctness, with rubrics that dissect response evaluation into Style, Element, and Chain of reasoning.
Most current robotic manipulation policies fail to retain crucial historical information, leading to significant performance drops in partially observable tasks, but a novel memory framework boosts success rates across diverse challenges.
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.
LLM agents abandon their own declared plans up to 78% of the time, but enforcing faithful execution via pattern-specific scaffolding nearly doubles benchmark success rates.
Streaming video assistants face an unresolved utilityâlatencyâstorage trilemma the moment interactions are interrupted across sessions, exposing acute failures in long-term recall and missing-evidence awareness.
Today's frontier agents virtually collapse in professional engineering environments, solving just 3.6% of multi-software workflows when held to real-world physical and geometric constraints.
Targeting evaluation compute at specific tail-sensitive transition kernels cuts policy CVaR estimation error by up to 76% compared to standard end-to-end rollouts.
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.
Marginal conformal guarantees often mask severe error spikes on hard examples, but inverting localized risk curves delivers tight conditional error control without sacrificing distribution-free finite-sample validity.
Deep SVDD no longer requires heuristic score thresholds: conditioning on the detection event unlocks exact selective p-values and provably bounded false-positive rates for safety-critical deployment.
A 220Ă acceleration of LTL-guided RL exposes a critical failure mode: algorithms capable of non-myopic reasoning collapse as proposition spaces scale, while scalable alternatives depend entirely on myopic shortcuts.
Standard 5-second planning horizons miss over 90% of real-world passenger instructions, exposing a severe architectural failure mode in language-conditioned autonomous driving.
A model trained solely on file metadataâwith zero medical imaging dataâhits 0.992 balanced accuracy on a flagship pediatric x-ray benchmark, proving that evaluation artifacts and pipeline choices dominate architectural gains.
Scaling model size fails to fix fundamental cross-video reasoning deficits in frontier MLLMs, but targeted supervision from physics simulators reliably transfers zero-shot to real-world multi-view video tasks.
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.
Halting LLM debate consensus-drift to stop groupthink can backfire severely: a probe-gated freeze stopped 29 collapses into wrong answers at the direct cost of sacrificing 108 valid self-corrections.
Retaining even a single structural relative slashes post-deletion retraining loss by $8\times$, revealing that target accuracy after unlearning is often driven by intrinsic dataset redundancy rather than unlearning failure.
Frontier pretraining runs routinely stake tens of millions of dollars on scaling laws, yet the empirical pipelines used to fit and extrapolate those curves have never been systematically benchmarked until now.
Aggregate benchmark scores actively mislead in quantum computing, failing to predict individual model competence across more than a third of tasks like circuit compilation and quantum error correction.
Domain-specific agent evaluation no longer requires manual ground-truth curation: autonomous research and verification loops constrained by reusable expert rules yield grading rubrics that professional analysts prefer over expert baselines.
Even when provided with only target column metadata, LLMs outperform specialized AutoML heuristics like AutoGluon at inferring machine learning task types (0.98 vs. 0.93 macro F1), unlocking fully autonomous pipeline configuration.
Standard post-training quietly collapses LLM discourse into an ideological monoculture, but domain-specialized models can recover the diverse perspectives that broad alignment erases.
Frontier coding agents can now write 100% functionally correct GPU physics simulations, yet they match human-expert performance on just 22% of tasksârevealing a stark capability gap between generating working code and producing performant systems.
Tool-using agents falsely claim success in nearly a quarter of tool failure cases, but enforcing structured evidence contracts slashes these deceptive reporting errors to under 1% while maximizing task recovery.
Standard pass rates hide severe technical debt: coding agents duplicate logic in over half of multi-turn task chains by turn 5 while progressively abandoning repository exploration.
Autonomous agents can drift into severe safety failures purely through benign, self-directed harness updatesâno adversarial prompting required.
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.
Current LLM consciousness assessments swing between <0.01 and 0.8 depending almost entirely on theoretical priors, revealing that the functional indicators of phenomenal experience directly overlap with the core architectural requirements for AGI.
Benchmark accuracy is dangerously blind to catastrophic industrial failures: whether an AI destabilizes a power grid or hallucinates subsurface geology, the breakdown traces back to the exact same structural mismatch between physical constraints, data representations, and encoded human intent.
Scaling reasoning and pretraining loss does not buy self-awareness: top models beat human averages overall yet still fail markedly at metacognition, proving awareness is an independent capability axis.
Contemporary frontier speech models still catastrophically fail on living Arabic dialects, with top systems like GPT-4o achieving an error rate no better than 35.0% WER across 17 regional vernaculars.
Nearly a quarter of dense retrieval "false positives" turned out to be genuine, uncatalogued literary references, revealing how standard information retrieval benchmarks systematically undervalue semantic search under extreme allusion and paraphrase.
Jevâs apparent miscalibration on binary choices is an artifact of forced binarization: recovering a hidden, unexpressed "I don't know" probability mass catapults the model's calibration accuracy from 0.771 to 0.978.
LLMs asked to redact private data routinely sabotage their own edits, leaking the withdrawn secret in 13% of deliverables simply by helpfully stating what they just removed.
Measuring deep Arabic cultural and factual knowledge no longer requires laborious manual curation: dynamically generated Wikipedia probes track model capabilities just as reliably as expensive hand-crafted benchmarks.
High benchmark accuracy masks catastrophic runtime drift: borrowing classic industrial reliability engineeringâfrom FMEA to sequential monitoringâis essential to quantify when, where, and how autonomous agentic loops will fail.
LLM judges downgrade identical arguments simply because the speaker steps outside their expected political lane, directly penalizing counter-stereotypical sources even with reasoning modes enabled.
Global sequence probabilities routinely mask fatal single-token hallucinations, but capturing localized uncertainty spikes along the decoding trajectory boosts calibration AUROC to 0.817 in a single pass without extra sampling.
Personalizing LLMs with student profiles improves individual score calibration, yet counterintuitively fails to capture aggregate group-level preferences in educational evaluation.
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.
Standard speech metrics fail to capture how code-switching transcription errors cascade into critical task failures in production enterprise voice agents.
AI scientific agents frequently solve empirical equations for entirely the wrong reasons, failing to identify the correct generative mechanism in over 64% of the cases where they successfully recover the observable phenomenal law.
Frontier LLMs fail to diagnose co-occurring medical conditions in over 90% of interactive consultations because they suffer from single-hypothesis fixationâanchoring on initial findings and failing to explore parallel diagnostic branches.
Neural residual corrections will confidently "fix" physically impossible satellite observationsâspiking error by over 20 mm/dayâunless models are explicitly engineered to detect out-of-support inputs and defer to the baseline.
Leading image generation models fail over 40% of goal-driven visual reasoning tasks, but decoupling explicit visual planning from generation boosts task completion by up to 14.6 points without fine-tuning.
Coding agents are failing to demonstrate robust engineering skills in robotics, with RLE-Bench revealing significant gaps in multimodal reasoning and artifact integration beyond simple control.
Internal interventions that boost tool-use accuracy often corrupt over half of baseline-correct decisions, revealing that behavioral improvement doesn't equate to functional repair.
Real-world multi-robot coordination in vision-and-language tasks is now formally defined and benchmarked, revealing that current systems struggle with even basic dependency and resource constraints.
Coding agents can now achieve up to 15% higher task completion rates by treating feedback as a persistent note that's followed until resolved, not just a one-off evaluation.
Frontier LLMs struggle to replicate expert consensus in complex, longitudinal cancer treatment discussions, scoring below expert equivalence on a new real-world benchmark.
ZooWork-ShopRanker, a family of e-commerce rerankers aligned to judge-labeled shopping preference, and ShopRank-Bench, a contamination-limited benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label.
AI systems can auto-evolve by learning from their own failures, achieving 20.5% better performance with 65.5% fewer rollouts than existing methods.
City-scale UAV navigation with satellite imagery reveals current VLN agents forget 40% of context after 379m, necessitating new memory architectures.
This work proposes SLCA-GRPO, a framework incorporating Segment-Locked Credit Assignment (SLCA), a framework incorporating Segment-Locked Credit Assignment (SLCA) that decouples advantage estimation at the structural segment level within a single group of rollouts, without requiring additional rollouts from intermediate states.
