ArXiv, the preprint server that revolutionized scientific communication, faces challenges in maintaining its relevance and functionality amidst exponential growth. While its open-access model democratized knowledge sharing, it now grapples with scaling its infrastructure, managing the deluge of submissions, and ensuring quality control without stifling innovation. The article explores ArXiv's history, highlighting its humble beginnings and its current struggles with limited resources and a volunteer-driven moderation system. Ultimately, ArXiv must navigate the complexities of evolving scientific practices and adapt its systems to ensure it continues to serve as a vital tool for scientific progress.
This paper introduces a novel method for inferring the "phylogenetic" relationships between large language models (LLMs), treating their development like the evolution of species. By analyzing the outputs of various LLMs on a standardized set of tasks, the researchers construct a distance matrix reflecting the similarity of their behaviors. This matrix then informs the creation of a phylogenetic tree, visually representing the inferred evolutionary relationships. The resulting tree reveals clusters of models based on their architectural similarities and training data, providing insights into the influence of these factors on LLM behavior. This approach offers a new perspective on understanding the development and diversification of LLMs, moving beyond simple performance comparisons to explore the deeper connections between them.
Several Hacker News commenters express skepticism about the paper's methodology and conclusions. Some doubt the reliability of using log-likelihoods on cherry-picked datasets to infer relationships, suggesting it's more a measure of dataset similarity than true model ancestry. Others question the assumption that LLMs even have a meaningful "phylogeny" like biological organisms, given their development process. The idea of "model paleontology" is met with both interest and doubt, with some arguing that internal model parameters would offer more robust insights than behavioral comparisons. There's also discussion on the limitations of relying solely on public data and the potential biases introduced by fine-tuning. A few commenters raise ethical concerns around potential misuse of such analysis for IP infringement claims, highlighting the difference between code lineage and learned knowledge.
arXiv is migrating its infrastructure from Cornell University servers to Google Cloud. This move aims to enhance arXiv's long-term sustainability, improve performance and scalability, and leverage Google's expertise in areas like security, storage, and machine learning. The transition will happen in phases, starting with a pilot program. arXiv emphasizes its commitment to remaining open and community-driven, with its operational control staying independent. They are also actively hiring for several roles, including software engineers and system administrators, to support this significant change.
Hacker News users discuss arXiv's move to Google Cloud, expressing concerns about potential vendor lock-in and the implications for long-term data preservation. Some question the cost-effectiveness of the transition, suggesting Cornell's existing infrastructure might have been sufficient with modernization. Others highlight the potential benefits of Google's expertise in scaling and reliability, but emphasize the importance of maintaining open access and avoiding proprietary formats. The need for transparency regarding the terms of the agreement with Google is also a recurring theme, alongside worries about potential censorship or influence from Google on arXiv's content. Several commenters note the irony of a pre-print server initially designed to bypass traditional publishing now relying on a large tech company.
The BitNet b1.58 technical report details a novel approach to data transmission over existing twisted-pair cabling, aiming to significantly increase bandwidth while maintaining compatibility with legacy Ethernet. It introduces 2B4T line coding, which transmits two bits of data using four ternary symbols, enabling a theoretical bandwidth of 1.58 Gbps over Cat5e and 6a cabling. The report outlines the 2B4T encoding scheme, discusses the implementation details of the physical layer transceiver, including equalization and clock recovery, and presents experimental results validating the claimed performance improvements in terms of data rate and reach. The authors demonstrate successful transmission at the target 1.58 Gbps over 100 meters of Cat6a cable, concluding that BitNet b1.58 offers a compelling alternative to existing solutions for higher-bandwidth networking on installed infrastructure.
HN users discuss BitNet, a new Ethernet PHY aiming for 1.58 Tbps over existing cabling. Several express skepticism that it's achievable, citing potential issues with signal integrity, power consumption, and the complexity of DSP required. One commenter highlights the lack of information on FEC and its overhead. Others compare it to previous ambitious, ultimately unsuccessful, high-speed Ethernet projects. Some are cautiously optimistic, acknowledging the significant technical hurdles while expressing interest in seeing further development and independent verification. The limited real-world applicability with current switch ASIC capabilities is also noted. Overall, the sentiment leans towards cautious skepticism, tempered by curiosity about the technical details and potential future advancements.
Researchers introduce Teukten-7B, a new family of 7-billion parameter language models specifically trained on a diverse European dataset. The models, Teukten-7B-Base and Teukten-7B-Instruct, aim to address the underrepresentation of European languages and cultures in existing LLMs. Teukten-7B-Base is a general-purpose model, while Teukten-7B-Instruct is fine-tuned for instruction following. The models are pre-trained on a multilingual dataset heavily weighted towards European languages and demonstrate competitive performance compared to existing models of similar size, especially on European-centric benchmarks and tasks. The researchers emphasize the importance of developing LLMs rooted in diverse cultural contexts and release Teukten-7B under a permissive license to foster further research and development within the European AI community.
Hacker News users discussed the potential impact of the Teukens models, particularly their smaller size and focus on European languages, making them more accessible for researchers and individuals with limited resources. Several commenters expressed skepticism about the claimed performance, especially given the lack of public access and limited evaluation details. Others questioned the novelty, pointing out existing multilingual models and suggesting the main contribution might be the data collection process. The discussion also touched on the importance of open-sourcing models and the challenges of evaluating LLMs, particularly in non-English languages. Some users anticipated further analysis and comparisons once the models are publicly available.
NoProp introduces a novel method for training neural networks that eliminates both backpropagation and forward propagation. Instead of relying on gradient-based updates, it uses a direct feedback mechanism based on a layer's contribution to the network's output error. This contribution is estimated by randomly perturbing the layer's output and observing the resulting change in the loss function. These perturbations and loss changes are used to directly adjust the layer's weights without explicitly calculating gradients. This approach simplifies the training process and potentially opens up new possibilities for hardware acceleration and network architectures.
Hacker News users discuss the implications of NoProp, questioning its practicality and scalability. Several commenters express skepticism about its performance on complex tasks compared to backpropagation, particularly regarding computational cost and the "hyperparameter hell" it might introduce. Some highlight the potential for NoProp to enable training on analog hardware and its theoretical interest, while others point to similarities with other direct feedback alignment methods. The biological plausibility of NoProp also sparks debate, with some arguing that it offers a more realistic model of learning in biological systems than backpropagation. Overall, there's cautious optimism tempered by concerns about the method's actual effectiveness and the need for further research.
Search-R1 introduces a novel method for training Large Language Models (LLMs) to effectively use search engines for complex reasoning tasks. By combining reinforcement learning with retrieval augmented generation, Search-R1 learns to formulate optimal search queries, evaluate the returned search results, and integrate the relevant information into its responses. This approach allows the model to access up-to-date, factual information and demonstrate improved performance on tasks requiring reasoning and knowledge beyond its initial training data. Specifically, Search-R1 iteratively refines its search queries based on feedback from a reward model that assesses the quality and relevance of retrieved information, ultimately producing more accurate and comprehensive answers.
Hacker News users discussed the implications of training LLMs to use search engines, expressing both excitement and concern. Several commenters saw this as a crucial step towards more factual and up-to-date LLMs, praising the approach of using reinforcement learning from human feedback. Some highlighted the potential for reducing hallucinations and improving the reliability of generated information. However, others worried about potential downsides, such as increased centralization of information access through specific search engines and the possibility of LLMs manipulating search results or becoming overly reliant on them, hindering the development of true reasoning capabilities. The ethical implications of LLMs potentially gaming search engine algorithms were also raised. A few commenters questioned the novelty of the approach, pointing to existing work in this area.
"Matrix Calculus (For Machine Learning and Beyond)" offers a comprehensive guide to matrix calculus, specifically tailored for its applications in machine learning. It covers foundational concepts like derivatives, gradients, Jacobians, Hessians, and their properties, emphasizing practical computation and usage over rigorous proofs. The resource presents various techniques for matrix differentiation, including the numerator-layout and denominator-layout conventions, and connects these theoretical underpinnings to real-world machine learning scenarios like backpropagation and optimization algorithms. It also delves into more advanced topics such as vectorization, chain rule applications, and handling higher-order derivatives, providing numerous examples and clear explanations throughout to facilitate understanding and application.
Hacker News users discussed the accessibility and practicality of the linked matrix calculus resource. Several commenters appreciated its clear explanations and examples, particularly for those without a strong math background. Some found the focus on differentials beneficial for understanding backpropagation and optimization algorithms. However, others argued that automatic differentiation makes manual matrix calculus less crucial in modern machine learning, questioning the resource's overall relevance. A few users also pointed out the existence of other similar resources, suggesting alternative learning paths. The overall sentiment leaned towards cautious praise, acknowledging the resource's quality while debating its necessity in the current machine learning landscape.
Block Diffusion introduces a novel generative modeling framework that bridges the gap between autoregressive and diffusion models. It operates by iteratively generating blocks of data, using a diffusion process within each block while maintaining autoregressive dependencies between blocks. This allows the model to capture both local (within-block) and global (between-block) structures in the data. By controlling the block size, Block Diffusion offers a flexible trade-off between the computational efficiency of autoregressive models and the generative quality of diffusion models. Larger block sizes lean towards diffusion-like behavior, while smaller blocks approach autoregressive generation. Experiments on image, audio, and video generation demonstrate Block Diffusion's ability to achieve competitive performance compared to state-of-the-art models in both domains.
HN users discuss the tradeoffs between autoregressive and diffusion models for image generation, with the Block Diffusion paper presented as a potential bridge between the two. Some express skepticism about the practical benefits, questioning whether the proposed method truly offers significant improvements in speed or quality compared to existing techniques. Others are more optimistic, highlighting the innovative approach of combining block-wise autoregressive modeling with diffusion, and see potential for future development. The computational cost and complexity of training these models are also brought up as a concern, particularly for researchers with limited resources. Several commenters note the increasing trend of combining different generative model architectures, suggesting this paper fits within a larger movement toward hybrid approaches.
Ladder is a novel approach for improving large language model (LLM) performance on complex tasks by recursively decomposing problems into smaller, more manageable subproblems. The model generates a plan to solve the main problem, breaking it down into subproblems which are then individually tackled. Solutions to subproblems are then combined, potentially through further decomposition and synthesis steps, until a final solution to the original problem is reached. This recursive decomposition process, which mimics human problem-solving strategies, enables LLMs to address tasks exceeding their direct capabilities. The approach is evaluated on various mathematical reasoning and programming tasks, demonstrating significant performance improvements compared to standard prompting methods.
Several Hacker News commenters express skepticism about the Ladder paper's claims of self-improvement in LLMs. Some question the novelty of recursively decomposing problems, pointing out that it's a standard technique in computer science and that LLMs already implicitly use it. Others are concerned about the evaluation metrics, suggesting that measuring performance on decomposed subtasks doesn't necessarily translate to improved overall performance or generalization. A few commenters find the idea interesting but remain cautious, waiting for further research and independent verification of the results. The limited number of comments indicates a relatively low level of engagement with the post compared to other popular Hacker News threads.
The paper "Is this the simplest (and most surprising) sorting algorithm ever?" introduces the "Sleep Sort" algorithm, a conceptually simple, albeit impractical, sorting method. It relies on spawning a separate thread for each element to be sorted. Each thread sleeps for a duration proportional to the element's value and then outputs the element. Thus, smaller elements are outputted first, resulting in a sorted sequence. While intriguing in its simplicity, Sleep Sort's correctness depends on precise timing and suffers from significant limitations, including poor performance for large datasets, inability to handle negative or duplicate values directly, and reliance on system-specific thread scheduling. Its main contribution is as a thought-provoking curiosity rather than a practical sorting algorithm.
Hacker News users discuss the "Mirror Sort" algorithm, expressing skepticism about its novelty and practicality. Several commenters point out prior art, referencing similar algorithms like "Odd-Even Sort" and existing work on sorting networks. There's debate about the algorithm's true complexity, with some arguing the reliance on median-finding hides significant cost. Others question the value of minimizing comparisons when other operations, like swaps or data movement, dominate the performance in real-world scenarios. The overall sentiment leans towards viewing "Mirror Sort" as an interesting theoretical exercise rather than a practical breakthrough. A few users note its potential educational value for understanding sorting network concepts.
Researchers report observing room-temperature superconductivity (above 400K) in graphite powder samples. They claim to have isolated superconducting particles from non-superconducting graphite by applying a magnetic field gradient, which levitated a small fraction of the material. These levitated particles exhibited diamagnetic behavior consistent with the Meissner effect, a key characteristic of superconductors. While the observed effect is intriguing, the authors acknowledge the need for further investigation and independent verification to confirm these extraordinary claims.
Hacker News users discussed the extraordinary claims of room-temperature superconductivity in the linked arXiv preprint with heavy skepticism. Several commenters pointed to the lack of details about the experimental setup and methodology, making replication difficult. The unusual magnetic sorting technique employed raised questions, with some suggesting it might be separating impurities rather than different superconducting phases. Others highlighted the history of similar unsubstantiated claims of room-temperature superconductivity, leading to a general atmosphere of "wait and see." A few commenters offered alternative explanations for the observed phenomena, including ferromagnetism or diamagnetism in impurities. Overall, the prevailing sentiment was cautious disbelief pending further evidence and scrutiny from the scientific community.
Large language models (LLMs) can improve their future prediction abilities through self-improvement loops involving world modeling and action planning. Researchers demonstrated this by tasking LLMs with predicting future states in a simulated text-based environment. The LLMs initially used their internal knowledge, then refined their predictions by taking actions, observing the outcomes, and updating their world models based on these experiences. This iterative process allows the models to learn the dynamics of the environment and significantly improve the accuracy of their future predictions, exceeding the performance of supervised learning methods trained on environment logs. This research highlights the potential of LLMs to learn complex systems and make accurate predictions through active interaction and adaptation, even with limited initial knowledge of the environment.
Hacker News users discuss the implications of LLMs learning to predict the future by self-improving their world models. Some express skepticism, questioning whether "predicting the future" is an accurate framing, arguing it's more akin to sophisticated pattern matching within a limited context. Others find the research promising, highlighting the potential for LLMs to reason and plan more effectively. There's concern about the potential for these models to develop undesirable biases or become overly reliant on simulated data. The ethics of allowing LLMs to interact and potentially manipulate real-world systems are also raised. Several commenters debate the meaning of intelligence and consciousness in the context of these advancements, with some suggesting this work represents a significant step toward more general AI. A few users delve into technical details, discussing the specific methods used in the research and potential limitations.
This paper investigates how pre-trained large language models (LLMs) perform integer addition. It finds that LLMs, despite lacking explicit training on arithmetic, learn to leverage positional encoding based on Fourier features to represent numbers internally. This allows them to achieve surprisingly good accuracy on addition tasks, particularly within the range of numbers present in their training data. The authors demonstrate this by analyzing attention patterns and comparing LLM performance with models using alternative positional encodings. They also show how manipulating or ablating these Fourier features directly impacts the models' ability to add, strongly suggesting that LLMs have implicitly learned a form of Fourier-based arithmetic.
Hacker News users discussed the surprising finding that LLMs appear to use Fourier features internally to perform addition, as indicated by the linked paper. Several commenters expressed fascination with this emergent behavior, highlighting how LLMs discover and utilize mathematical concepts without explicit instruction. Some questioned the paper's methodology and the strength of its conclusions, suggesting alternative explanations or calling for further research to solidify the claims. A few users also discussed the broader implications of this discovery for understanding how LLMs function and how they might be improved. The potential link to the Fourier-based positional encoding used in Transformer models was also noted as a possible contributing factor.
ArXivTok presents arXiv research papers in a short-video format, aiming to make complex topics more accessible. The site leverages AI to summarize papers and generates engaging videos with visuals, voiceover narration, and background music. This allows users to quickly grasp the core ideas of a paper without needing to delve into the full text, offering a faster and potentially more engaging way to explore scientific research.
HN users generally praised ArXivTok for its accessibility, making dense academic papers more digestible. Several commenters appreciated the use of TikTok's format, highlighting its effectiveness in quickly conveying complex information. Some expressed concern over potential simplification or misrepresentation of research, but the prevailing sentiment was positive, viewing ArXivTok as a valuable tool for disseminating scientific knowledge to a wider audience and sparking curiosity. A few users suggested improvements like linking directly to the original papers and providing more context around the research being presented. There was also discussion about the broader implications of using social media platforms like TikTok for scientific communication.
This paper explores the potential of Large Language Models (LLMs) as tools for mathematicians. It examines how LLMs can assist with tasks like generating conjectures, finding proofs, simplifying expressions, and translating between mathematical formalisms. While acknowledging current limitations such as occasional inaccuracies and a lack of deep mathematical understanding, the authors demonstrate LLMs' usefulness in exploring mathematical ideas, automating tedious tasks, and providing educational support. They argue that future development focusing on formal reasoning and symbolic computation could significantly enhance LLMs' capabilities, ultimately leading to a more symbiotic relationship between mathematicians and AI. The paper also discusses the ethical implications of using LLMs in mathematics, including concerns about plagiarism and the potential displacement of human mathematicians.
Hacker News users discussed the potential for LLMs to assist mathematicians, but also expressed skepticism. Some commenters highlighted LLMs' current weaknesses in formal logic and rigorous proof construction, suggesting they're more useful for brainstorming or generating initial ideas than for producing finalized proofs. Others pointed out the importance of human intuition and creativity in mathematics, which LLMs currently lack. The discussion also touched upon the potential for LLMs to democratize access to mathematical knowledge and the possibility of future advancements enabling more sophisticated mathematical reasoning by AI. There was some debate about the specific examples provided in the paper, with some users questioning their significance. Overall, the sentiment was cautiously optimistic, acknowledging the potential but emphasizing the limitations of current LLMs in the field of mathematics.
The arXiv LaTeX Cleaner is a tool that automatically cleans up LaTeX source code for submission to arXiv, improving compliance and reducing potential processing errors. It addresses common issues like removing disallowed commands, fixing figure path problems, and converting EPS figures to PDF. The cleaner also standardizes fonts, removes unnecessary packages, and reduces file sizes, ultimately streamlining the arXiv submission process and promoting wider paper accessibility.
Hacker News users generally praised the arXiv LaTeX cleaner for its potential to improve the consistency and readability of submitted papers. Several commenters highlighted the tool's ability to strip unnecessary packages and commands, leading to smaller file sizes and faster processing. Some expressed hope that this would become a standard pre-submission step, while others were more cautious, pointing to the possibility of unintended consequences like breaking custom formatting or introducing subtle errors. The ability to remove comments was also a point of discussion, with some finding it useful for cleaning up draft versions before submission, while others worried about losing valuable context. A few commenters suggested additional features, like converting EPS figures to PDF and adding a DOI badge to the title page. Overall, the reception was positive, with many seeing the tool as a valuable contribution to the academic writing process.
This paper proposes a new quantum Fourier transform (QFT) algorithm that significantly reduces the circuit depth compared to the standard implementation. By leveraging a recursive structure and exploiting the symmetries inherent in the QFT matrix, the authors achieve a depth of O(log * n + log log n), where n is the number of qubits and log * denotes the iterated logarithm. This improvement represents an exponential speedup in depth compared to the O(log² n) depth of the standard QFT while maintaining the same asymptotic gate complexity. The proposed algorithm promises faster and more efficient quantum computations that rely on the QFT, particularly in near-term quantum computers where circuit depth is a crucial limiting factor.
Hacker News users discussed the potential impact of a faster Quantum Fourier Transform (QFT). Some expressed skepticism about the practicality due to the significant overhead of classical computation still required and questioned if this specific improvement truly addressed the bottleneck in quantum algorithms. Others were more optimistic, highlighting the mathematical elegance of the proposed approach and its potential to unlock new applications if the classical overhead can be mitigated in the future. Several commenters also debated the relevance of asymptotic complexity improvements given the current state of quantum hardware, with some arguing that more practical advancements are needed before these theoretical gains become significant. There was also a brief discussion regarding the paper's notation and clarity.
Summary of Comments ( 24 )
https://news.ycombinator.com/item?id=43738478
Hacker News users discuss ArXiv's impact and challenges. Several commenters praise its role in democratizing scientific communication and accelerating research dissemination. Some express concern over the lack of peer review, leading to the spread of unverified or low-quality work, while acknowledging the tradeoff with speed and accessibility. The increasing volume of submissions is mentioned as a growing problem, making it harder to find relevant papers. A few users suggest potential improvements, such as enhanced search functionality and community-driven filtering or rating systems. Others highlight the importance of ArXiv's role as a preprint server, emphasizing that proper peer review still happens at the journal level. The lack of funding and the difficulty of maintaining such a crucial service are also discussed.
The Hacker News post "Inside ArXiv" (https://news.ycombinator.com/item?id=43738478) has generated a significant discussion with a variety of viewpoints on arXiv's role, impact, and challenges.
Several commenters discuss the importance of arXiv as a preprint server, enabling rapid dissemination of research and fostering collaboration. One commenter points out its crucial role in fields beyond computer science, particularly physics and mathematics, where it's been a cornerstone of academic communication for decades. This is contrasted with the slower, more traditional publishing routes. Another commenter emphasizes the democratizing effect of arXiv, allowing researchers outside of prestigious institutions to share their work and gain recognition.
The moderation policies of arXiv and the potential for biases are also a recurring theme. Some users express concerns about rejections and the subjective nature of the process, while others defend the need for moderation to maintain quality and prevent the spread of pseudoscience or unsubstantiated claims. The difficulties in striking a balance between open access and quality control are acknowledged. Specific examples of controversial submissions and their handling are mentioned, highlighting the complexities involved.
The conversation also delves into the technical aspects of arXiv, such as its outdated interface and the challenges of searching and navigating the vast repository of papers. Suggestions for improvements, including better search functionality and more modern design, are put forth. The need for better categorization and tagging of papers to facilitate discovery is also mentioned.
Another thread discusses the future of arXiv, and the potential for alternative platforms or decentralized models to emerge. The role of institutional backing and funding is discussed, along with the possibilities and challenges of community-driven initiatives. The importance of preserving the core values of open access and accessibility while adapting to the evolving needs of the scientific community is emphasized.
Finally, several comments focus on the article itself, critiquing its focus and perspective. Some find the article too superficial or lacking in depth, while others appreciate its overview of arXiv's history and impact. The lack of discussion about specific technical challenges and the moderation process is also noted.