A developer built TideForge, a desktop app that converts chat corrections into LoRA training data. Each model reply has a "Teach" button; corrections accumulate as JSONL and trigger PEFT fine-tuning on your base model. Initial test: 110 hand-written corrections on Qwen 0.6B, loss dropped 4.25→0.73, adapter maintained identity across ~30 jailbreak prompts. Free, Windows, GGUF-compatible.
An Engram module (O(1) hash-keyed associative memory) injected into Transformers for autoregressive image generation on ImageNet 256×256 fails to improve quality (FID) despite FLOP gains. Gate-clamp, donor-probe, and frozen-table experiments show the module acts as a gated architectural side-pathway, not a content-addressed retrieval mechanism.
LATTE is a personalization framework for frozen LLMs that forecasts user preference trajectories by subtracting comparable peer profiles. A lightweight sequence predictor forecasts the next state, injected via a single anchored soft token. On Amazon Reviews 2023, LATTE achieves ROUGE-L=0.259 vs 0.219 for static profiles.
PushCen-ADFL is an asynchronous decentralized federated learning framework reducing communication by 80% while improving accuracy by up to 6% under data heterogeneity. It employs shared centroid representation, average-preserving push-sum mixing, and lightweight centroid regularization to correct aggregation bias and mitigate model drift.
Systematic study of HiF8 W8A8 QAT on OpenPangu-Embedded-1B. Identifies two failure modes: amax saturation (silent corruption via clipping) and catastrophic forgetting (aggressive learning rate overwrites knowledge). Solutions: 64-step history window for DTS and 500-step BF16 warmup. Results: 0.43% MMLU drop, 0.58% HellaSwag, 0.22% ARC-Challenge vs baseline.
HRVConformer is a hybrid Convolution-Transformer architecture for classifying neonatal hypoxic-ischemic encephalopathy from raw heart rate signals. Trained on 1,573 epochs (259 expert-annotated, rest weakly labelled), the model achieves 83.23% AUC and 74.56% accuracy on a 215-hour test set, outperforming ResNet50 and Transformer baselines.
AIMS-Fold integrates structural proteomics data (XL-MS, HDX-MS) into a guided-diffusion framework to predict protein complex structures. The framework outperforms Boltz-2 on induced proximity targets, critical for antibody and PROTAC design.
PathoFM, an encoder-centric transformer pretrained on clinical time series (pathological gait analysis for spinal cord injury), combines three objectives: Local Completion, Temporal Continuity, and Unsupervised In-Context Dynamics. The study shows that dynamics-centric objectives produce the most balanced transferable representations across classification and regression tasks.
New method to learn dynamics of high-dimensional probability densities without labeled trajectories. Two-parameter flows learn sampling-time transports from base to marginals, then extract physics-time dynamics via regression on coupled synthetic trajectories. Scalable to high dimensions, handles rotational physics phenomena.
Modular framework to enhance signed GNNs with temporal context. Introduces HCIM (Historical Context Integration Module) combining learnable temporal weighting, LSTM, and multi-head attention for link prediction in temporal signed networks. Evaluated on Bitcoin OTC, Bitcoin Alpha, Reddit with statistically significant improvements over static baseline.
MultiSeismo is a multimodal seismic dataset of 16K+ events (2010-2023) integrating waveform recordings, intensity maps, geographical data, and textual metadata. Authors develop SeisModal, a specialized model based on Unified IO 2 with timeseries encoder, outperforming generic GMMs on multimodal seismic reasoning tasks.
New diagnostic metric for learned physics simulators: semigroup error measures temporal consistency by comparing direct evolution over s+t with composed evolution (s then t). Tested on heat and Burgers dynamics with ConvNet and FNO baselines, Spearman correlation ρ=0.635 with rollout degradation. Useful as post-hoc evaluation rather than universal training objective.
An arXiv study reveals that correct demonstrations can degrade in-context learning (ICL) performance. Researchers introduce task-preserving perturbations to show that correctness does not guarantee utility: changing an exemplar's input while keeping a correct output can reduce accuracy, especially for smaller models and harder tasks.
CroCo extends contrastive preference tuning on self-generations to 14 languages (high and low-resource). A reward model trained on English preferences produces useful within-language rankings across languages without language-specific annotation. Gains confirmed on EuroLLM-9B and Aya-3B with on-policy data.
Study on cultural value alignment in LLMs via activation steering. Researchers bypass safety refusals using 300 situational dilemmas to extract latent cultural values, then apply activation steering without retraining. Key finding: cultural values are encoded as coupled structures, limiting precise alignment.
arXiv study on anti-autistic ableism detection in LLMs. Researchers propose a psychometrically-weighted evaluation framework anchored in annotator positionality. Models produce harmful outputs, mislabel community-reclaimed language, and rely on surface-level keyword matching rather than context (speaker identity, in-group solidarity vs. out-group harm).
Adaptive learning framework using LLMs grounded in expert domain knowledge to deliver just-in-time pedagogical feedback. Deployed in a university course (N>1000), it improves student performance by 80% by analyzing reasoning essays and correcting conceptual errors through iterative LLM conversations.
ReDose is a dataset of 6,435 Reddit posts annotated by toxicologists to extract DRUG, DOSE, and EFFECT entities. BiomedBERT achieves F1=0.843 for DRUG; Llama-3 70B outperforms GPT-4 (F1=0.79 vs 0.72). EFFECT extraction remains challenging (GPT-4 recall=0.41).
Study of 20,000 stories from 4 LLMs: 11 words (Elias, Mara, Elara, lighthouse, clockmaker, librarian) appear in 88.3% of generated narratives. These tokens originate from preference data used during alignment, not training data. Reveals disproportionate impact of small datasets combined with powerful alignment algorithms.
LieEDNN introduces Lie group embedded dynamical neural networks to model continuous symmetries on manifolds. The method addresses incompatibility between neural network addition and non-Euclidean geometry through adjoint Lie group actions on Lie algebras. Tested on SE(3) for telescopic manipulator control.
Study showing that evaluation methods for AI use in scientific publications produce significant biases when ignoring contextual differences across countries and fields. Pooled benchmarks conflate pre-existing stylistic variation with LLM-generated text, overestimating AI in some contexts and underestimating it in others.
NEI-CAP, a diagnostic protocol to audit the construction of "Not Enough Information" labels in fact verification benchmarks. Researchers show NEI competence does not transfer reliably across constructions: models trained on shortcut-prone evidence conditions fail to recognize semantically related insufficient evidence. Tested on SciFact, FEVER, and HoVer.
Theoretical and empirical investigation of sequential knowledge editing mechanisms in LLMs. Authors prove formal equivalence between one-time and sequential editing, demonstrating stability emerges naturally without complex regularizations. Code released.
Theoretical paper on sequence models' insufficiency when facing unobserved latent states. Authors formalize a mixed-regime process where a perfect predictor becomes overconfident if observed context matches the wrong latent regime. They show the sufficiency gap can only be closed by perfect revelation of latent state or equivalent verification mechanism.
POLAR is a framework for MLLM-based embodied agents that personalizes assistance through a multimodal knowledge graph. It organizes past interactions into semantic memory (visual concepts) and episodic memory (agent trajectories), improving performance especially for multi-hop reasoning and tracking user-specific context updates.
Two agentic AI frameworks for scientific workflows: DeepTS/DeepCollector automates large-scale curation and deduplication of time-series datasets; DeepScribe autonomously analyzes complex physics lectures to generate structured reports. Hybrid Local Body/Remote Brain architecture via Google Colab with Python orchestrators and cloud LLM backends.
A framework for managing uncertainty in LLM-generated procedures for virtual laboratory planning in educational settings. The system uses structured domain representations and uncertain LLM-generated state-transition samples to extract procedural rules, transform them into explicit constraints, and repair defective procedural steps.
CARL, an offline hierarchical reinforcement learning algorithm, exploits local dynamics regularity to discover reusable skills. By aligning global contexts with required action sequences, the method improves performance on OGBench when integrated with HIQL.
Theoretical and empirical work on training LLM-based dialogue agents. Identifies context distribution shift as fundamental limitation of Static Context RL and Interactive RL. Proposes Calibrated Interactive RL combining interactive RL with simulator alignment to reduce sim-to-real gap and improve multi-turn dialogue quality.
arXiv study on legal LLM evaluation. Existing models are sensitive to legally irrelevant variations. LexGuard, an adversarial multi-agent framework, formalizes statutes into executable constraints and uses SMT solvers to verify legal satisfaction and logical consistency.
Tail-Aware HiFloat4 applies W4A4 post-training quantization to the Wan2.2 text-to-video generation model. The method adapts ViDiT-Q using HiFloat4 format, quantizes transformer linear layers, preserves numerically sensitive modules in high precision, and introduces activation-tail-aware percentile calibration to reduce impact of rare outliers.
Study of policy-gradient method failures in long-horizon decision problems with cumulative damage. Authors identify two orthogonal failure modes and propose decomposition separating completion (reaching terminal horizon) and optimality (matching dynamic programming). Experiments on bricklayer career (49 steps) and NBA forward career (20 seasons).
Paper on multi-stakeholder LLM alignment. Holistic judges conflate utility estimation and aggregation, creating unstable weighting noise. DecompR decouples counterfactual-calibrated weights (fixed before candidate scoring) from independent per-role utility estimation, removing candidate-dependent weight drift and reducing estimation noise.
Conv-to-Bench automatically converts multi-turn user-assistant dialogues into structured evaluation checklists for code tasks. The framework achieves Spearman correlation ρ=1.000 with BigCodeBench, with human agreement κ=0.705 for LLM-as-a-judge evaluation.
AGORA introduces inference-free prompt compression for LLM agents using adapter-grounded observation-action retention. Standard token-level extractive compressors fail on agents (75% performance in 8/9 cases). Ablation shows structure and adaptive scorer enable 1.0-11.5x compression from fixed keep ratio.
SilIF augments Isolation Forest for fraud detection by adding a silhouette-based scoring layer computed from tree path lengths. On IEEE-CIS benchmark (~590K transactions, 3.5% fraud), SilIF achieves +0.0080 AUC-PR improvement over plain IF (p=0.046). No gains on Sparkov dataset; paper characterizes when the augmentation helps.
Online framework modeling streaming time series as dynamic mixtures of time-delay systems. Uses compact tensor representation of Markov parameters to capture system dynamics and input delays, with tensor decomposition for rapid regime adaptation. DelayMix outperforms baselines on real non-stationary data with superior forecast accuracy and faster delay adaptation.
CoAD, a novel framework for time series anomaly detection, unifies classification (Outlier Exposure) and reconstruction (Masked Autoencoder) paradigms. The classification module generates probability-informed soft masks for the reconstruction module, addressing generalization and masking misalignment issues. Experiments on standard benchmarks demonstrate significant improvements with faster inference.
UAV detection system using sound signals with rational Gaussian wavelet neural networks for adaptive feature extraction. Classifies single drones and swarms while maintaining interpretability. Outperforms traditional ML approaches in indoor and outdoor environments. Implementation publicly available.
Study on RL agent adaptation in gradually non-stationary environments. Authors modify 3D Miniworld and MuJoCo environments to introduce continuous drift, showing that synaptic consolidation applied to multi-timescale Successor Features outperforms Q-value-based approaches. Stability outweighs plasticity in continual learning with gradual changes.