Estimating Rankings via Pairwise LLM Comparisons and MCMC
Instead of long one-shot rankings, use pairwise LLM judgments and Bradley–Terry with Bayesian MCMC to estimate ranks and uncertainty.
Humanoids, autonomy, and embodied AI.
Hub content is updated incrementally.
Instead of long one-shot rankings, use pairwise LLM judgments and Bradley–Terry with Bayesian MCMC to estimate ranks and uncertainty.
Summarizes LAW: learnable per-pixel loss reweighting to address spatial imbalance in medical diffusion and segmentation, improving FID.
Explain why 4-bit quantized models can show lower PPL than FP16, and outline a reproducible evaluation protocol.
Model Spec’s chain of command can override custom instructions, causing persona and reasoning drift. Design priorities, exceptions, and fallbacks to improve reproducibility.
A curated link roundup from recently collected official updates and tech news.
Real-user data shows CAPTCHA time varies by context, while ML and relay attacks raise friction without guaranteed security gains.
A 3.5B-token combustion knowledgebase and CombustionQA benchmark unify knowledge injection and evaluation into one pipeline.
Assesses zero-shot MLLMs for video anomaly detection, focusing on false alarms/misses, prompt specificity, 1–3s clips, and PR/F1 evaluation.
EVMbench evaluates agent smart-contract security across detection, patching with tests, and exploit attempts in a sandboxed EVM.
SPIRIT uses deep perception uncertainty to gate shared autonomy, switching between semi-autonomous manipulation and haptic teleoperation.
How to reduce anthropomorphism, overconfidence, and hallucinations by structuring work as claim-evidence-verification checklists.
How LegalBench evaluates legal LLM reasoning beyond accuracy, emphasizing justification and auditability through structured argumentation and governance.
Logi-PAR (arXiv:2603.05184v1) integrates neural-guided differentiable rules into clinical PAR, enabling rule traces and counterfactual interventions.
A practical look at memory admission control for LLM agents, reducing long-term memory pollution while improving auditability and metrics.
In multimodal clinical reasoning, reported gains don’t guarantee safety; prioritize controlled evaluation, grounding, and auditable failure modes.
PDF-to-Excel results vary by upload limits and text vs visual parsing. Use structure metrics and fixed schemas for fair evaluation.
SOLID proposes mask-conditioned diffusion to learn/evaluate spatiotemporal fields from sparse moving sensors without dense ground truth, emphasizing calibrated uncertainty.
How web search and reasoning modes trade off accuracy, reproducibility, and latency—plus a simple test procedure to verify results yourself.
A curated link roundup from recently collected official updates and tech news.
If/Then guide to AI coding quota marketplaces: structure roles, avoid key-transfer violations, and add SSDF-style verification.
For long policy reports, context and upload limits push chunked workflows that separate evidence retrieval from drafting, improving traceability and quality.
A shift from IDE plugins to terminal-native CLI coding agents, highlighting AGENTS.md and context pipelines that shape reliability and verification loops.
Study compares six post-training 2-bit methods on a Polish 11B LLM, highlighting gaps between benchmarks and generation stability.
Why single success rates fail for long-running agents, and how to measure goal drift, consistency, and governance stability in HAT.