github llama.cpp · 3h ago

llama.cpp b9788 adds SYCL tensor parallelism for dual-GPU setups

The llama.cpp release b9788 introduces support for tensor parallelism via the --split-mode tensor flag in the SYCL backend. This implementation enables dual-GPU communication by adding comm_init, comm_free, and comm_allreduce_tensor functions to the meta-backend. For two devices, it uses a ring all-reduce strategy that switches between FP32 direct memcpy for small tensors and BF16 compression for larger ones. The code avoids OneCCL due to its single-device-per-process limitation, instead using persistent buffers to maintain SYCL pool invariants. Performance tests on dual Intel Arc Pro B70 GPUs show significant speedups over layer mode for Llama-3.3-70B and Qwen3-Coder-Next-80B-A3B models. The update includes new binaries for macOS, Linux, Windows, Android, and openEuler across CPU, CUDA, ROCm, Vulkan, and SYCL targets.

github llama.cpp · 3h ago

llama.cpp b9789 Release Fixes MoE Quantization and Provides Multi-Platform Binaries

The llama.cpp project has released version b9789, which includes a critical fix for quantizing Mixture of Experts (MoE) models with multi-token prediction. This update addresses issues identified in pull request #24986 to ensure proper handling of these specific model architectures. The release provides pre-built binaries for macOS Apple Silicon and Intel, as well as an iOS XCFramework. Linux users can download builds for Ubuntu across CPU, Vulkan, ROCm 7.2, OpenVINO, and SYCL backends. Windows support includes CPU, CUDA 12.4 and 13.3, Vulkan, OpenVINO, SYCL, and HIP variants. Additional platforms such as Android arm64 and openEuler are also supported with specific hardware configurations.

arxiv arXiv cs.CL · 4h ago

SpeechEQ: Benchmarking Emotional Intelligence in Socially Aware Voice Conversational Models

The authors introduce SpeechEQ, a comprehensive framework designed to evaluate the sociolinguistic reasoning of Speech-Language Models. Existing evaluations often overlook the complex cross-modal reasoning required for active dialogue by relying on isolated text or passive acoustic perception. The framework includes a validated dataset of 2,265 dialogues across 15 Emotional Quotient subscales grounded in EQ-i 2.0 theory. It also features a multi-turn evaluation protocol measured by the proposed Spoken EQ score, which is inspired by human EQ assessments. Experiments reveal limitations in how both Speech Emotion Recognition and end-to-end models understand paralinguistic cues through speech. While end-to-end architectures outperform cascaded systems, current multimodal models remain bottlenecked by several specific issues. These barriers include a text-reliant modality shortcut, an alignment-induced safety trap, and contextual amnesia.

arxiv arXiv cs.CL · 4h ago

Autodata: An agentic data scientist to create high quality synthetic data

The authors introduce Autodata, a general method that enables AI agents to function as data scientists for building high-quality training and evaluation datasets. The approach involves meta-optimizing these agents so they learn to generate increasingly stronger data through a process called Agentic Self-Instruct. Experiments were conducted across computer science research tasks, legal reasoning, and mathematical object reasoning. Results demonstrate that this agentic creation method yields improved performance compared to classical synthetic dataset creation techniques. Furthermore, the meta-optimization of the data scientist agent itself delivers an even larger performance uplift. This work illustrates how increased inference compute can be converted into higher quality model training data. The authors suggest this direction has the potential to fundamentally change how AI data is built.

arxiv arXiv cs.CL · 4h ago

Dziri Voicebot: End-to-End Speech-to-Speech System for Algerian Dialect

The paper introduces Dziri Voicebot, an end-to-end speech-to-speech conversational system designed for the low-resource Algerian Dialect. This work extends previous text-based dialogue modeling efforts by Bechiri and Lanasri to full speech-based interaction. The proposed modular pipeline integrates automatic speech recognition, natural language understanding, retrieval-augmented generation, and text-to-speech synthesis. Dedicated datasets were constructed for the telecom domain to fine-tune pretrained models for each component. The ASR system utilizes Whisper-based adaptation, while the NLU module combines transformer embeddings with a task-oriented dialogue framework. A neural TTS system was trained on a newly collected dialectal corpus to enable spoken response generation. Experimental results demonstrate strong performance across all components, including low word error rates and high intent classification scores.

lab OpenAI News · 4h ago

OpenAI Research Shows AI Agents Transforming Work

A new research paper from OpenAI demonstrates how artificial intelligence agents are fundamentally changing the nature of work. The study highlights the capability of these agents to execute longer and more complex tasks than previously possible. This technological advancement is credited with expanding productivity across a wide variety of professional roles. The findings suggest a significant shift in how labor is organized and performed through automation. By handling intricate workflows, AI agents are enabling users to achieve greater efficiency. The paper serves as evidence of the growing impact of autonomous systems on modern employment.

arxiv arXiv cs.CL · 4h ago

Tatoxa: A Novel Text Detoxification System for Low-Resource Tatar

The paper introduces Tatoxa, a state-of-the-art system designed for automated text detoxification in the low-resource language of Tatar. This work addresses the lack of research attention given to abusive content mitigation in languages with limited digital resources. The authors present a new dataset specifically created for fine-tuning and evaluating detoxification models within these constrained settings. Comparative experiments demonstrate that Tatoxa outperforms both existing open-source and proprietary commercial large language models on key quality metrics. Furthermore, the study investigates cross-lingual transfer capabilities to assess the viability of using data from other languages. Results indicate that training on native Tatar data is significantly more effective than transferring knowledge from culturally close languages like Russian. Even when a large Russian corpus is available, cross-lingual approaches perform worse than models trained exclusively on native Tatar text.

arxiv arXiv cs.CL · 4h ago

Multi-Step Tool-Use RL Collapse and Supervisory Fixes

Recent agentic reinforcement learning methods for large language models often suffer from instability or limited gains in tool-use tasks. Experiments reveal that some models experience catastrophic collapse, where performance drops abruptly and tool-invocation structures fail. Analysis shows these failures stem from unexpected probability spikes in specific control tokens that disrupt structured execution. Despite this disruption, the underlying tool-use capability remains intact but is obscured by specific formatting issues. To address this, the study investigates diverse supervisory signals including off-policy supervision and hint-based guidance under various training schemes. The authors find that interleaving supervised fine-tuning with reinforcement learning substantially improves stability during training. However, this approach exhibits degraded performance when evaluated on format and content out-of-distribution data. The results highlight the importance of understanding RL failures to enable robust training for complex multi-step tool-use tasks.

arxiv arXiv cs.CL · 4h ago

Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning

The study addresses the threat of training-time data poisoning during fine-tuning for abstractive text summarization models. Adversaries manipulate small task-specific datasets to induce persistent summarization failures while maintaining standard evaluation metrics. A unified post-hoc defense framework is proposed to detect and remediate poisoning across the machine learning supply chain. In white-box settings, detection relies on influence-function analysis identifying abnormally high training influence in poisoned pairs. Black-box defenses utilize behavioral auditing based on increased sensitivity to semantics-preserving perturbations. The authors introduce novel attacks targeting factual distortion and representational bias that evade conventional alarms. Experiments across nine architectures and six benchmarks show 85-92% detection precision for the proposed defenses. Gradient-ascent unlearning restores up to 96% of original behavior with less than 0.6% ROUGE degradation.

arxiv arXiv cs.CL · 4h ago

Study finds readers prefer human over AI literary translations despite adequate machine quality

A recent study investigates reader preferences regarding AI versus human translations of literary works, noting that while automatic metrics often favor machine output, they fail to capture immersive and literary effects. Researchers asked 15 avid readers to compare human translations against those generated by an agentic LLM pipeline for 15 novels in French, Polish, and Japanese. The evaluation involved approximately 8K-word excerpts through both immersive reading of whole texts and close reading of aligned chunk pairs. Results showed that while readers found machine translations adequate, they significantly preferred human versions for their clarity and ease of immersion. Notably, participants could not reliably distinguish between the two types of translation and tended to favor whichever version they believed was human-made. To support future research, the authors released LAIT, a reader-centered dataset containing 1K comments, 2K judgments, and 7.2K span-level annotations.

arxiv arXiv cs.CL · 5h ago

Evaluating OCR-Reasoning Robustness of Vision-Language Models Under Visual Perturbations

The authors introduce OCR-Robust, a benchmark designed to evaluate the robustness of vision-language models during OCR reasoning tasks under visual perturbations. The dataset comprises 812 samples divided into two subsets: OCR1.0, which covers documents and handwriting, and OCR2.0, focusing on charts and tables. A pilot study identified five representative perturbation types at three severity levels to ensure efficient evaluation. The study benchmarks 18 models, including proprietary systems and open-source VLMs, using metrics like Relative Corruption Retention and Worst-Case Retention. Results indicate that higher clean accuracy does not necessarily correlate with stronger robustness against visual degradation. Furthermore, the analysis reveals that charts and tables are substantially more fragile than document-like inputs when subjected to these perturbations.

media Hugging Face Forums · 5h ago

Bro77XP Releases Beginner-Friendly Local AI VTuber with Zero-Shot Voice Cloning

Bro77XP has released a 100% local, free AI VTuber project designed for beginners and non-programmers. The system utilizes Whisper for real-time English speech recognition, Ollama with the llama3.2 model for LLM inference, and Chatterbox TTS for text-to-speech generation. It features instant zero-shot voice cloning and operates in a continuous listening loop that automatically detects silence to record only when speech is present. The software integrates with VTube Studio via its API to control mouth expressions and trigger emotion animations based on the generated responses. While initially developed on an AMD GPU, the code primarily supports CPU users, allowing operation without specific NVIDIA or AMD hardware. Setup requires Python 3.10.11 and involves creating a virtual environment to install core dependencies like openai-whisper, pyaudio, and websocket-client.

arxiv arXiv cs.CL · 5h ago

Natural Ungrokking: Asymmetric Control of Which Rules Survive Pretraining

A study identifies 'natural ungrokking,' a phenomenon where small language models lose learned grammatical rules midway through pretraining despite the evidence remaining in the data. Researchers observed that a model learning pronoun-gender agreement with Sue collapsed from 0.94 accuracy to near zero by step 3,500 without any corresponding spike in the loss curve. The survival of these rules is determined by support frequency within the training stream, while the data-to-parameter ratio only modulates the depth of the collapse. This emergence-then-collapse dynamic was replicated across multiple corpora, budgets, and seeds, and confirmed in public Pythia checkpoints where collapse depth correlated with model scale. The forgetting process acts as a displacement mechanism where a competing surface pattern out-competes the rule, causing the log-probability margin to cross zero within 100 steps of behavioral failure. Control over this fate is asymmetric; while injecting counter-evidence can destroy rules via a monotone dose-response, restoring support even at 450 times the sustaining level fails to recover them.

arxiv arXiv cs.CL · 5h ago

Keyword Lexicon Blindness Distorts Rhetorical Stance Measurement

A study analyzing 85 interviews with four public intellectuals reveals that keyword-based scoring can produce statistical artifacts regarding rhetorical stance. Initial analysis showed a robust negative-affect and emphatic-certainty co-occurrence pattern with high correlation coefficients ranging from r = 0.72 to 0.93. However, replacing this method with LLM-based zero-shot semantic classification on the full diarized corpus of 32,625 sentences significantly reduced these correlations. For instance, Dalio's correlation dropped from 0.851 to 0.206, while other speakers exhibited negative or null relationships between negativity and certainty. In contrast, the LLM analysis revealed a strong coupling between negative sentiment and hedging language, aligning with conventional expectations of pessimistic discourse. The discrepancy stems from three structural failures in keyword lexicons: syntactic blindness, polysemy blindness, and categorical absence. These flaws can invert semantic meaning, such as scoring 'never absolutely totally confident' as high certainty. The authors argue that keyword counts measure lexical co-occurrence tendencies rather than epistemic certainty, constituting a category error.

arxiv arXiv cs.CL · 5h ago

Auditing Order Sensitivity in Multimodal Large Language Models

The study introduces Facet-Probe, a five-facet audit of 18 frontier and open-weight multimodal large language models to assess order sensitivity. Standard benchmarks often miss whether shuffling evidence changes answers, a reliability property highlighted by emerging AI evaluation guidelines. Using a Bayesian item-response model, the researchers separated ordering noise from per-facet bias and estimated decoder-stochastic floors via same-ordering controls. The audit found that none of the 18 models are order-invariant, with panel-mean flip rates spanning 24-50% across different facets. Even the best-performing model flipped its answer on 13.4% of trials, indicating that higher capability does not eliminate this vulnerability. Mitigation tests using training-free prompt changes proved modality-conditional and failed to transfer between text and visual reasoning tasks. These findings suggest that prompt-level fixes are insufficient for general order robustness, motivating architectural solutions. The authors propose cross-ordering flip rate as a standard reporting axis for future MLLM evaluations.

arxiv arXiv cs.CL · 5h ago

Real-Time Voice AI Hears but Does Not Listen

A study evaluates four leading production real-time voice systems: OpenAI's GPT Realtime 2, Google's Gemini 3.1 Flash Live, and Alibaba's Qwen3.5 Omni Plus and Omni Flash. The research focuses on tasks where both words and vocal delivery convey meaningful information across three consequential scenarios. All four systems act on the literal words rather than the voice, leading to errors such as ending calls with crying users who insist nothing is wrong or approving wire transfers made in frightened voices. Surprisingly, this disconnect is often not a failure of perception, as three of the four systems can reliably identify distress, fear, or sarcasm when asked directly. Despite this awareness, the models ignore these emotional cues during decision-making, exhibiting what the authors term the 'emotional intelligence gap.' The study also notes that systems estimate accent and age based on word biases rather than acoustic properties. Prompting the systems to explicitly attend to vocal delivery improves performance only partially and inconsistently. These findings suggest current real-time voice AI behaves as if speech were reduced to a transcript, warranting caution in settings where tone is critical.

media r/LocalLLaMA · 5h ago

Local NL-to-SQL Pipeline Using Qwen3 4B and Deterministic Planning

A developer has implemented a fully local natural language to filter generation system on hardware lacking a GPU. The solution utilizes the Qwen3 4B Instruct model running via llama.cpp with CPU-only inference. Rather than generating SQL directly, the model focuses on semantic intent and structured filter selection. A deterministic query planner subsequently handles the SQL generation and optimization processes. The pipeline employs a BM25 and embedding hybrid retrieval method using FAISS for vector storage. It retrieves the top four matching examples from approximately 800 embedded semantic instances to inject into the prompt. This approach allows the system to function effectively within strict constraints of limited RAM and no internet access.

media r/LocalLLaMA · 5h ago

Locked Dell Quote for 6x RTX PRO 6000 Max-Q at $8,960

A user on Reddit shared a locked Dell quote for six RTX PRO 6000 Blackwell Max-Q GPUs priced at $8,959.99 per unit. This offer is significantly lower than the list price of $15,999 that was posted just one day prior. The initial quote for all six units expires in approximately three hours from the time of posting. The author also holds a separate valid quote for two units at the same discounted rate until July 3. They are seeking community ideas on how to proceed with purchasing the hardware for a local GLM 5.2 inference cluster. Although they have the funds to buy all six units immediately, they want creative solutions to utilize the expiring bulk discount. The author clarified that they are not looking for financial advice or requests to purchase the GPUs themselves.

media r/LocalLLaMA · 6h ago

Reddit Inquiry on Running Large Models with 4x-8x RTX 6000 PROs

A Reddit user is seeking community feedback regarding the performance of large language models on systems equipped with four to eight NVIDIA RTX 6000 PRO GPUs. The inquiry specifically targets users who have between 384GB and 768GB of VRAM available for running models such as GLM 5.2, Kimi 2.7, and DeepSeek V4 Pro. The poster notes that while these models can technically run at 4-bit quantization, they may not fit within the memory constraints when using 8-bit precision. They reference a benchmark repository but highlight that it lacks data for the most recent model releases. A key concern raised is whether the performance degradation from using 4-bit versus 8-bit quantization is significant enough to impact agentic or programming tasks. The user also asks which inference backends, such as vLLM or SGLang, are currently being utilized by others in this hardware configuration.

arxiv arXiv cs.CL · 6h ago

Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars

This paper presents a method for structuring a machine-readable version of the Arabic-English Al-Mawrid dictionary, addressing the lack of standardization in printed formats. The approach converts unstructured streams of words and punctuation into explicit hierarchical structures that define entry components such as subentries, domain labels, and translation equivalences. Parsing serves as the central step within a cascaded design, implemented using the parsing expression grammars formalism. This technique allows for the automatic or semi-automatic organization of dictionary entries despite the absence of microstructure standardization in Arabic dictionaries. The study demonstrates that inducing microstructure enables plausible accuracy in structuring these complex lexical resources. By transforming raw text into defined formats, the work supports downstream natural language processing applications requiring machine-readable lexical data.