Combining LLM Agents With Science Models for Reliable Loops
A practical pattern: LLMs handle planning and interpretation, while science models provide constraint-based scoring and stopping gates.
Signals, research, and debates around general intelligence and superintelligence.
Hub content is updated incrementally.
A practical pattern: LLMs handle planning and interpretation, while science models provide constraint-based scoring and stopping gates.
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.
Model Spec’s chain of command can override custom instructions, causing persona and reasoning drift. Design priorities, exceptions, and fallbacks to improve reproducibility.
SPIRIT uses deep perception uncertainty to gate shared autonomy, switching between semi-autonomous manipulation and haptic teleoperation.
How LegalBench evaluates legal LLM reasoning beyond accuracy, emphasizing justification and auditability through structured argumentation and governance.
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.
Cryo-SWAN is a voxel density-map VAE, reporting consistent reconstruction-quality gains across ModelNet40, BuildingNet, and ProteinNet3D.
If/Then guide to AI coding quota marketplaces: structure roles, avoid key-transfer violations, and add SSDF-style verification.
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.
VANGUARD estimates GSD from monocular UAV video using small vehicles as anchors to recover metric scale without GPS or telemetry.
CoT perturbations can sharply reduce accuracy. Unit conversion remains hard at scale; isolate checks and use self-consistency.
Retiring legacy ChatGPT models may shift tone, refusals, and creativity, reshaping the balance between expression and safety guardrails.
Examines multi-rater 3D lesion segmentation, limits of vanilla diffusion, and VDD anchored to consensus priors improving GED/CI.
How LLMs create difficulty illusions, and how to design evaluation gates with scenarios, protocols, and multi-metric reporting.
GIPO targets scarce, stale interaction data by replacing hard importance-ratio clipping with log-ratio Gaussian trust weights for stable reuse.
Reframes agentic AI failures as governance issues, proposing dual-helix governance with a Knowledge/Behavior/Skills architecture.
How LLM signals can shape belief in partially observable TAMP, and why calibration, uncertainty, and safety filters matter for reliability.
How ambiguity detection, clarification, and sycophancy control shape managerial AI advice quality, risk, and evaluation metrics.
MASS trains LLMs to synthesize per-problem data and self-update at test time, raising auditability, integrity, and reproducibility needs.
PlugMem externalizes long-term memory as a plug-in to reduce retrieval bloat and relevance loss, while highlighting persistent injection risks.
Tool-free visual puzzle claims depend on fixed constraints: lock tools, image preprocessing, prompts, and logs for reproducibility.