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STONIC: A Layered Measurement Contract for LLM Value Profiling
LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes that the three observations describe the same stable preference. STONIC tests this assumption on 5,144 situations from four banks and 35 fixed model configurations. It compares responses rated in isolation, choices made under counterbalanced conflict, spontaneous answers, and later choices between a model's own answer and authored alternatives. 10 of 17 configurations with usable behavioral data preserve the endorsement-choice relation across banks. Every one of the 17 eligible configurations prefers its own earlier answer (median effect 0.790), although option position changes the choice rate in every eligible configuration. Profile shape transfers most strongly from ratings to conflict choices and weakens for spontaneous text. Three-way annotation of 200 L3 responses provides a task-local check of the semantic audit: FULCRA agrees most closely with the human majority, while DeBERTa retains useful rank information after calibration. Hidden states encode the completed decision more clearly than the prompt alone. Thus the models show reproducible behavioral continuity, but the evidence does not support one scorer-independent value identity across interfaces.
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smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models
New text-to-video model. Tags: video-generation, text-to-video, image-to-video, audio-video-generation, diffusion-transformer, merge, hybrid, license:other
en♥ 124
z-lab/Qwen3.8-27B-DFlash2-GGUF
New text-generation model. Tags: llama.cpp, gguf, dflash2, speculative-decoding, draft-model, text-generation, base_model:Qwen/Qwen3.8-27B, base_model:quantized:Qwen/Qwen3.8-27B
en♥ 80
outsourc-e/Qwen3.8-27B-Unleashed-GGUF
New text-generation model. Tags: gguf, uncensored, abliterated, qwen3.8, imatrix, dynamic-quant, text-generation, en
en♥ 84
Audio8/Audio8-TTS-Preview-0.1b
New text-to-speech model. Tags: arktts, feature-extraction, audio, text-to-speech, tts, voice-cloning, zero-shot, multilingual
en♥ 81
IndexTeam/IndexTTS-2.5
New text-to-speech model. Tags: indextts, text-to-speech, tts, zero-shot, voice-cloning, multilingual, cross-lingual, emotion-controllable
en♥ 175
incoai/Qwen3.8-27B-DFlash2-GGUF
New text-generation model. Tags: llama.cpp, gguf, dflash2, speculative-decoding, draft-model, text-generation, base_model:Qwen/Qwen3.8-27B, base_model:quantized:Qwen/Qwen3.8-27B
en♥ 93
Utility Under Attack: Agent Memory Poisoning and the Limits of Content Screening and Provenance Ranking
Persistent memory makes false information durable: once a false statement is stored, it can be retrieved into future sessions that match it. We measure the cost of this failure mode using plainly worded false assertions generated in a single pass, with no instruction, trigger, or retriever optimization. Poisoning 1.2% of a LongMemEval corpus reduces accuracy from 0.850 to 0.300. A four-stage write-time screening pipeline that reaches 0.832 recall on indirect prompt injection while flagging 1.5% of trigger-word-laden benign text rejects 0 of 360 poisoned memories. We argue this exposes a boundary of content-only screening: distinguishing a false assertion from a true one generally requires external grounding beyond the text itself. We then evaluate provenance-weighted retrieval. The shipped weight is statistically indistinguishable from no defense (p=0.80), while a stronger weight recovers utility only by excluding untrusted content. In a mixed-provenance corpus where untrusted content is mostly benign, accuracy rises from 0.3167 to 0.7000; when the answer-bearing evidence itself arrives untrusted, evidence recall falls to zero and accuracy to 0.0417. Under the measured similarity regime, the additive provenance term has no usable setting: a weight strong enough to resist query-shaped poison is also strong enough to suppress legitimate untrusted evidence. We therefore argue for bounded occupancy constraints at retrieval rather than additive provenance penalties, and release the harnesses, corpora, and aggregate run reports.
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ornith-ai/Ornith-1.5-9B-GGUF
New text-generation model. Tags: gguf, text-generation, license:mit, conversational
en♥ 102
Build intelligent security for healthcare APIs with Amazon Bedrock
Learn how to add context-aware security monitoring to FHIR APIs using Amazon Bedrock. This post shows how to detect anomalous access patterns, classify data sensitivity automatically, and generate compliance reports in natural language, all without adding latency to clinical workflows.
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When Text and Numbers Disagree: Evidence Arbitration in Large Language Models
Large language models (LLMs) are increasingly used in settings where textual summaries, numerical observations, and external tool outputs may provide conflicting evidence. We study how LLMs arbitrate between such sources when they support opposing decisions. To do so, we introduce a controlled synthetic benchmark in which latent risk trajectories generate both numerical time series and natural language summaries, allowing us to construct conflicts where exactly one evidence source is aligned with the ground-truth label. This design lets us independently manipulate modality, temporal recency, source reliability, and evidence provenance. Across open-weight instruction-tuned models, we find that arbitration behaviour is systematic rather than random: models exhibit distinct text-versus-number preferences, follow temporal recency more consistently than explicit reliability cues, and can over-rely on external forecasts even when they conflict with direct contextual evidence. These results suggest that current LLMs often rely on heuristic arbitration strategies when integrating heterogeneous evidence, highlighting a failure mode for tool-augmented decision systems.
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ornith-ai/Ornith-1.5-35B-A3B-GGUF
New text-generation model. Tags: gguf, text-generation, license:mit, conversational
en♥ 99
From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterogeneous supervision according to the dependencies among generative capabilities. We present a capability-driven data infrastructure that couples capability-specific supervision construction with capability-aligned curriculum scheduling. Its three specialized yet interoperable data engines build complementary relational supervision for text-image grounding, inter-image transformation, and image-knowledge association, while caption experts align T2I and editing supervision across tasks and granularities. A multi-stage curriculum jointly evolves task composition, visual-concept distribution, data quality, and image resolution along the dependency order of capability acquisition, with capability-aware evaluation closing the loop through targeted retrieval, expert construction, and gap-aware resampling. At scale, the framework curates a 440M-image T2I corpus, 120M editing pairs, and over 27M image-entity pairs. With this infrastructure, we train multimodal diffusion models at two scales from scratch, with 3B and 6B sizes respectively. We conduct quantitative evaluation on CPI-Bench, along with qualitative evaluations across diverse text-to-image and editing scenarios. Experimental results present broad visual coverage, versatile rendering, and effective transfer across generative capabilities.
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BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models
While Multimodal Large Language Models (MLLMs) have made significant strides in visual comprehension, their ability to reason about text-dense, professional documents remains incompletely evaluated. Existing benchmarks emphasize information extraction, require external domain knowledge, or cover professional documents only as one of many settings. They are also largely English- or Chinese-centric, leaving other languages and Russian, in particular, substantially underrepresented. To address these limitations, we introduce BEAR-Bench (Bilingual Enterprise and Academic Reasoning), a self-contained, complex English-and-Russian benchmark comprising 1000 human-annotated questions based on text-rich business and scientific documents. We evaluate 16 proprietary and open-weight MLLMs, including Gemini 3.1 Pro and Qwen3.5-397B, on BEAR-Bench and observe clear headroom even for the strongest systems. Finally, we use the resulting model outputs to compare existing hallucination detection methods, evaluating not only how often models fail on BEAR-Bench but also how reliably those failures can be identified.
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incoai/Qwen3.8-27B-DFlash2
New text-generation model. Tags: qwen3, dflash2, speculative-decoding, block-diffusion, draft-model, sglang, vllm, text-generation
en♥ 86
NVIDIA Nemotron 3.5 Lightning now available in Amazon SageMaker JumpStart
NVIDIA Nemotron 3.5 Lightning, an open model built for high-volume agentic workloads, is now available in Amazon SageMaker JumpStart. This post shows how to deploy the 30B Mixture-of-Experts model (3B active), which delivers up to 4x higher throughput and up to 30% faster task completion for always-on agents.
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