4arXiv cs.LG (Machine Learning)·
Fruitfly-inspired classification-to-regression framework for nonlinear dynamical systems
A new arXiv preprint proposes a regression framework inspired by the fruitfly olfactory system, replacing global surrogate models with a finite library of local patterns and using similarity-weighted reconstruction for prediction. The method is applied to nonlinear dynamical systems, data-driven regression, and physics-informed learning. An offline-online workflow separates pattern extraction from inference, offering explicit control over accuracy-storage-compute tradeoffs.
Related guides (1)
Related events (8)
FFR extends Forward-Forward algorithm to regression tasks with 73% memory reduction
A new arXiv preprint introduces FFR (Forward-Forward for Regression), the first framework to adapt Hinton's Forward-Forward algorithm—a biologically plausible, backpropagation-free training method—to regression problems. FFR introduces an ordinal competitive goodness function, a stratified ladder architecture, and hierarchical prediction with uncertainty estimation to handle continuous target spaces. Across five real-world regression benchmarks, FFR recovers 98.6% of backpropagation accuracy while reducing peak training memory to 27% of BP's at depth 8 and 8% at depth 32, with per-iteration time around 72% of BP's.
Kolmogorov Regression lifts diffusion policies to Cameron-Martin space for robust long-horizon control
Researchers introduce a backward Kolmogorov equation framework that reformulates diffusion policy training as a deterministic boundary-value PDE problem in Cameron-Martin space, replacing stochastic score matching. The approach uses a precision-weighted Cameron-Martin loss and a Kolmogorov residual as an inference-time failure detector, yielding convergence guarantees tied to kernel effective rank rather than action dimension. Validation on the PushT manipulation benchmark shows 17% improvement in episode reward and 67.6% reduction in inter-step drift; a 6-station manufacturing scheduling task shows 28.4% lower RMSE than LSTM baselines and 96% reduction in deadlock events via Hamilton-Jacobi reachability certification.
Transformer-guided swarm intelligence framework for low-resource neural architecture search
A new arXiv preprint proposes a hybrid NAS framework combining an autoregressive Transformer controller trained via reinforcement learning with an Artificial Bee Colony metaheuristic for local search. The system targets consumer-grade hardware (NVIDIA RTX 3060), completing architecture search in ~3 hours and finding compact models: 84.85% on CIFAR-10 with ~174K parameters and F1=0.71 on fraud detection with ~4,600 parameters. A dynamic entropy mechanism is introduced to prevent premature convergence during RL training. The work addresses the democratization angle of NAS, positioning results as suitable for edge deployment.
Adaptive Digital Twin framework uses LoRA and drift detection for continual surrogate model validation
A new arXiv preprint presents a framework for self-adaptive Digital Twins that combines Fisher score-based drift detection, Low-Rank Adaptation (LoRA) for parameter-efficient continual learning, and Mann-Whitney U tests for online statistical validation of surrogate model updates. The system triggers fine-tuning of fewer than 1% of model parameters upon drift detection and certifies predictive improvement before deploying updated surrogates. Case studies in stochastic linear systems and directed energy deposition additive manufacturing demonstrate successful detection of distributional shifts with short delays. The work addresses the open challenge of maintaining surrogate model fidelity under concept drift in real-time physical system mirroring.
Self-evolving agent harness framework uses quality-diversity archive to generalize harness improvements across model families
A new arXiv preprint introduces a framework for automatically improving LLM agent harnesses (prompts, injected knowledge, runtime control, configuration) without modifying model weights. The system separates change proposal (done by an LLM diagnosing failures) from credit assignment (done by deterministic statistical testing), populating a gated quality-diversity archive keyed on failure pathology rather than task identity to prevent overfitting. Evaluated across seven domains with a frozen open-weight model, the framework achieves +9 to +15.5 percentage point gains on sealed test sets, retaining 86–147% of training gains, with the key finding that pathology-to-patch mappings transfer across model families.
SemRF: Semantic Reference Frames for analyzing residual-stream dynamics in language models
A new arXiv preprint introduces Semantic Reference Frames (SemRF), a formal framework for analyzing how language model computation evolves across transformer layers via the residual stream. The method uses anchor-based coordinates with pseudo-inverse tying to separate semantic measurement from residual dynamics, enabling stable cross-layer comparison without measurement drift. The framework defines layerwise semantic trajectories, Voronoi-based coarse cell assignments, and a minimum-action canonical trace, with theoretical links to parameter efficiency and knowledge density. This is a mechanistic interpretability contribution aimed at understanding internal model computation.
OAT: Unsupervised failure attribution for LLM agents via one-class learning on successful trajectories
A new arXiv preprint introduces OAT, a method for attributing failures in LLM-based agentic systems by training exclusively on successful trajectories using one-class learning with neural controlled differential equations. At inference time, each step in a failure trajectory receives an anomaly score based on deviation from learned success dynamics. OAT is reported to be 200–5000× faster than prompting-based baselines while outperforming them by +20% F1 in-domain and +7% out-of-distribution, using only 100 successful trajectories for training. The approach addresses a practical bottleneck in agentic system debugging by eliminating the need for costly step-level failure annotations.
KAFFEE: Addressing the Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
This paper identifies a 'dynamic-probabilistic consistency (DPC) gap' in dynamical systems reconstruction (DSR), where optimizing finite-horizon probabilistic objectives can degrade learned dynamics or decouple predictive uncertainty from local tangent dynamics. Three failure mechanisms are isolated: core collapse, noise masking, and blind uncertainty. The authors propose KAFFEE, a differentiable extended Kalman filter-based training framework that evaluates likelihood on local predictive residuals while transporting covariance through learned Jacobians, reducing these failure modes on stochastic hyperchaotic Lorenz-96 and across 13 chaotic systems when adapting a DSR foundation model.