v0.16.1
Axolotl v0.16.1 Release Notes Gemma 4 Support Example YAML: https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/gemma4/26b-a4b-moe-qlora.yaml gemma4 support by @winglian in #3574 Full Changelog : v0.16.0...v0.16.1
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ROMAN: A Multiscale Routing Operator for Convolutional Time Series Models
arXiv:2604.02577v1 Announce Type: new Abstract: We introduce ROMAN (ROuting Multiscale representAtioN), a deterministic operator for time series that maps temporal scale and coarse temporal position into an explicit channel structure while reducing sequence length. ROMAN builds an anti-aliased multiscale pyramid, extracts fixed-length windows from each scale, and stacks them as pseudochannels, yielding a compact representation on which standard convolutional classifiers can operate. In this way, ROMAN provides a simple mechanism to control the inductive bias of downstream models: it can reduce temporal invariance, make temporal pooling implicitly coarse-position-aware, and expose multiscale interactions through channel mixing, while often improving computational efficiency by shortening th
DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning
arXiv:2604.02694v1 Announce Type: new Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety. Existing forensic methods mainly rely on visual cues and lack evidence-based reasoning to reveal subtle text manipulations. Detection, localization, and explanation are often treated as isolated tasks, limiting reliability and interpretability. To tackle these challenges, we propose DocShield, the first unified framework formulating text-centric forgery analysis as a visual-logical co-reasoning problem. At its core, a novel Cross-Cues-aware Chain of Thought (CCT) mechanism enables implicit agentic reasoning, iteratively cross-validating visual anomalies with textual semantics to produce consistent, e
Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents
arXiv:2604.03173v1 Announce Type: new Abstract: Large language models and deep research agents supply citation URLs to support their claims, yet the reliability of these citations has not been systematically measured. We address six research questions about citation URL validity using 10 models and agents on DRBench (53,090 URLs) and 3 models on ExpertQA (168,021 URLs across 32 academic fields). We find that 3--13\% of citation URLs are hallucinated -- they have no record in the Wayback Machine and likely never existed -- while 5--18\% are non-resolving overall. Deep research agents generate substantially more citations per query than search-augmented LLMs but hallucinate URLs at higher rates. Domain effects are pronounced: non-resolving rates range from 5.4\% (Business) to 11.4\% (Theolog
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I tested speculative decoding on my home GPU cluster. Here's why it didn't help.
I spent Saturday night testing n-gram speculative decoding on consumer GPUs. The claim: speculative decoding can speed up LLM inference by 2-3x by predicting future tokens and verifying them in parallel. I wanted to see if that holds up on real hardware running diverse workloads. For the most part, it doesn't. But the journey was worth it, and I caught a benchmarking pitfall that I think a lot of people are falling into. The setup My home lab runs Kubernetes on a machine called Shadowstack. Two NVIDIA RTX 5060 Ti GPUs (16GB VRAM each, 32GB total). I use LLMKube, an open source K8s operator I built, to manage LLM inference workloads with llama.cpp. For this test I deployed two models: Gemma 4 26B-A4B : Google's Mixture of Experts model. 26B total params but only ~4B active per token. Runs a
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