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The author shares early progress on Leo/PSCLS, an experimental system that learns sequence relationships and improves its story generation and metrics as it is trained on more stories.
Lophius is a new hybrid code/GUI research system for language models that runs inside a notebook, aiming to reduce boilerplate and streamline tasks like model inspection, tokenizer analysis, and inference.
This paper introduces MameLoshnLM, the first open-source 8B-parameter Yiddish language model, along with the Oytser pretraining corpus and Kashes evaluation benchmark. It demonstrates that continued pretraining on high-quality Yiddish data outperforms general multilingual models, highlighting the value of dedicated low-resource language modeling.
This paper introduces CGTime, a 4B-parameter computation-grounded time-series-language model that decouples perception from description using deterministic statistics and LLM verbalization, outperforming larger general-purpose models on multivariate understanding tasks.
WAR Enterprise released WARMIND-200M V2, a 203M-parameter Portuguese-first causal language model with public weights and local CPU inference, intended as a research checkpoint to validate their full training pipeline.
AntLingAGI announces Ling 3.0 flash, a native hybrid-linear reasoning model with 124B total parameters and 5.1B active, MIT-licensed, claiming to match a 1T flagship with much less compute and faster response times.
Introduces Maglev, a recurrent Transformer architecture with fixed-size memory that generalizes sliding-window attention while remaining parallelizable during training. It uses a prefiller and decoder with a memory consistency loss, improving validation loss and downstream benchmarks over baselines.
This paper introduces AdaMTP, an adaptive training paradigm for multi-token prediction that dynamically aligns prediction horizons with sequence predictability using entropy-based segmentation, consistently outperforming standard MTP on math, code, and general benchmarks across three LLM backbones.
DiffusionGemma is an experimental open-weight language model that generates text via discrete diffusion rather than token-by-token decoding, enabling exceptionally high-speed generation.
Presents DLLM-TTS, a block discrete diffusion language model for text-to-speech synthesis that processes X-Codec2 tokens in blocks, enabling parallel generation with RTF 0.15 while achieving competitive quality with only 20K hours of training data.
AI9Stars released G9v3-39A5B, an open-weights 39B MoE language model with 5 active experts, targeting reasoning, coding, and assistant tasks under Apache 2.0.
The article introduces PARALLEL, a prefrontal-aligned reinforcement-inspired approach for language-model learning that determines when and how strongly to adapt to each sample, using separate controller signals to improve adaptation efficiency while retaining high performance.
AURORA-LM introduces a continuous-latent diffusion language model that separates decodable text representation from distribution modeling, achieving strong performance on OpenWebText and XSum while scaling to 1B parameters.
This paper introduces Dual-Anchored Policy Distillation (DAPD), a framework to address privilege illusion in on-policy self-distillation for language models. DAPD improves Qwen3-4B by +2.00 points on average across tasks, with gains persisting at larger scales.
Smallest.ai raises $13M in Series A funding to develop small, specialized voice models enabling real-time, human-like conversation for AI agents, aiming to make voice interactions indistinguishable from human speech.
Introduces MORFES, a benchmark of 500 expert-verified items for testing productive inflectional competence in Modern Greek, and evaluates open language models including their own Sophea-Genesis-1, which leads on inflectional morphology.
MUGEN introduces a unified motion-language framework that avoids discrete codebooks and iterative decoding, using a single adaptive-length autoencoder with continuous latent slots and one-shot generation to achieve efficient, high-quality text-to-motion and motion-to-text performance across HumanML3D and SnapMoGen benchmarks.
Inkling-Small, a 12B active parameter model with 276B total parameters, is released with full open weights, matching Inkling's performance on reasoning and agentic tasks at a quarter the size.
A comparison between AntLing 3.0 flash, MiniMax M2.7, and Step 3.7 flash models, likely evaluating performance and identifying the true 'flash' model.
This paper introduces constitutional midtraining, inserting values-based content into the midtraining phase of large language models, and shows that it produces more durable alignment gains compared to post-training methods, with benefits persisting after fine-tuning. The approach incurs no capability cost and improves resistance to blackmail and other alignment pressures.