Committee

arXiv:2411.16525v2 Announce Type: replace-cross Abstract: We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on emph{single-head} transformers with only a emph{single} self-attention layer: (i) is universal, and (ii) supports efficient (even almost-linear time) algorithms under the Strong Exponential Time Hypothesis (SETH). Statistically, we prove that prompt tuning on such simplest possible transformers are universal approximators for sequence-to-sequence Lipschitz functions. In addition, we provide an exponential-in-$dL$ and -in-$(1/epsilon)$ lower bound on the required soft-prompt tokens for prompt tuning to memorize any dataset with 1-layer, 1-head transformers. Computationally, we identify a phase transition in the efficiency of prompt tuning, determined by the norm of the emph{soft-prompt-induced} keys and queries, and provide an upper bound criterion. Beyond this criterion, no sub-quadratic (efficient) algorithm for prompt tuning exists under SETH. Within this criterion, we showcase our theory by proving the existence of almost-linear time prompt tuning inference algorithms. These fundamental limits provide important necessary conditions for designing expressive and efficient prompt tuning methods for practitioners.

A major hurdle in using AI for genomics is the lack of interpretable, step-by-step reasoning from complex DNA data. While DNA foundation models excel at learning rich sequence patterns for tasks such as variant prediction and gene regulation, they often operate as black boxes, offering limited insight into the underlying biological mechanisms. Meanwhile, large language models demonstrate impressive reasoning skills across various domains, but they aren’t designed to handle raw genomic sequences. This gap between strong DNA representation and deep biological reasoning prevents AI from reaching expert-level understanding and limits its potential to drive scientific discovery through meaningful, hypothesis-driven explanations.  DNA foundation models have made significant progress by learning rich representations directly from genomic sequences, showing strong performance across a range of biological tasks. Models like Evo2, with its long-range capabilities, highlight their potential, but their lack of interpretability limits deeper biological insights. Meanwhile, large language models excel in reasoning over biomedical texts but often don’t engage directly with raw genomic data. Attempts, such as GeneGPT and TxGemma, represent early efforts to bridge this gap. Current genomic benchmarks assess task performance but fall short in evaluating reasoning and hypothesis generation.  Researchers from the University of Toronto, Vector Institute, University Health Network (UHN), Arc Institute, Cohere, University of California, San Francisco, and Google DeepMind have introduced BIOREASON, a pioneering AI system that unites a DNA foundation model with an LLM. This integration allows BIOREASON to analyze raw genomic sequences while applying LLM-based reasoning to generate clear, biologically grounded insights. Trained through supervised fine-tuning and reinforcement learning, it achieves a performance gain of 15% or more over traditional models, reaching up to 97% accuracy in KEGG-based disease pathway prediction. This approach offers interpretable, step-by-step outputs that advance biological understanding and facilitate hypothesis generation.  The BIOREASON model is a multimodal framework designed to support deep, interpretable biological reasoning by combining genomic sequences with natural language queries. It uses a DNA foundation model to extract rich, contextual embeddings from raw DNA inputs and integrates these with tokenized textual queries to form a unified input for a LLM, specifically Qwen3. The system is trained to generate step-by-step explanations of biological processes. DNA embeddings are projected into the LLM’s space using a learnable layer, and the combined input is enriched with positional encoding. Additionally, reinforcement learning via Group Relative Policy Optimization refines its reasoning capabilities.  The researchers evaluated BIOREASON on three datasets focused on DNA variant interpretation and biological reasoning. It outperformed both DNA-only and LLM-only models in predicting disease outcomes from genomic variants. The best-performing version, which combined Evo2 and Qwen3-4B, achieved high accuracy and F1-scores across all tasks. A notable case study involved a PFN1 mutation linked to ALS, where BIOREASON accurately predicted the disease and generated a 10-step explanation tracing the variant’s impact on actin dynamics and motor neuron degeneration. This shows its strength not just in accurate predictions but also in providing transparent, biologically grounded reasoning paths.  In conclusion, BIOREASON combines DNA encoders with large language models to enable detailed, interpretable reasoning over genomic data. Unlike traditional models, it not only makes accurate predictions but also explains the biological logic behind them using step-by-step outputs. This helps scientists better understand disease mechanisms and generate new research questions. While powerful, BIOREASON has challenges, like high computational costs and limited uncertainty measures. Future work aims to address these issues by improving scalability, incorporating additional biological data such as RNA and proteins, and applying it to broader tasks, including GWAS. Overall, BIOREASON shows promise in advancing precision medicine and genomic research.  Check out the Paper, GitHub Page and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. The post Meet BioReason: The World’s First Reasoning Model in Biology that Enables AI to Reason about Genomics like a Biology Expert appeared first on MarkTechPost.

Like people undergoing cognitive migration, institutions must reassess what they were made for in this age of AI.Read More

Introduction: The Need for Dynamic AI Research Assistants Conversational AI has rapidly evolved beyond basic chatbot frameworks. However, most large language models (LLMs) still suffer from a critical limitation—they generate responses based only on static training data, lacking the ability to self-identify knowledge gaps or perform real-time information synthesis. As a result, these models often deliver incomplete or outdated answers, particularly for evolving or niche topics. To overcome these issues, AI agents must go beyond passive querying. They need to recognize informational gaps, perform autonomous web searches, validate results, and refine responses—effectively mimicking a human research assistant. Google’s Full-Stack Research Agent: Gemini 2.5 + LangGraph Google, in collaboration with contributors from Hugging Face and other open-source communities, has developed a full-stack research agent stack designed to solve this problem. Built with a React frontend and a FastAPI + LangGraph backend, this system combines language generation with intelligent control flow and dynamic web search. The research agent stack utilizes the Gemini 2.5 API to process user queries, generating structured search terms. It then performs recursive search-and-reflection cycles using the Google Search API, verifying whether each result sufficiently answers the original query. This iterative process continues until the agent generates a validated, well-cited response. Architecture Overview: Developer-Friendly and Extensible Frontend: Built with Vite + React, offering hot reloading and clean module separation. Backend: Powered by Python (3.8+), FastAPI, and LangGraph, enabling decision control, evaluation loops, and autonomous query refinement. Key Directories: The agent logic resides in backend/src/agent/graph.py, while UI components are structured under frontend/. Local Setup: Requires Node.js, Python, and a Gemini API Key. Run with make dev, or launch frontend/backend separately. Endpoints: Backend API: http://127.0.0.1:2024 Frontend UI: http://localhost:5173 This separation of concerns ensures that developers can easily modify the agent’s behavior or UI presentation, making the project suitable for global research teams and tech developers alike. Technical Highlights and Performance Reflective Looping: The LangGraph agent evaluates search results and identifies coverage gaps, autonomously refining queries without human intervention. Delayed Response Synthesis: The AI waits until it gathers sufficient information before generating an answer. Source Citations: Answers include embedded hyperlinks to original sources, improving trust and traceability. Use Cases: Ideal for academic research, enterprise knowledge bases, technical support bots, and consulting tools where accuracy and validation matter. Why It Matters: A Step Towards Autonomous Web Research This system illustrates how autonomous reasoning and search synthesis can be integrated directly into LLM workflows. The agent doesn’t just respond—it investigates, verifies, and adapts. This reflects a broader shift in AI development: from stateless Q&A bots to real-time reasoning agents. The agent enables developers, researchers, and enterprises in regions such as North America, Europe, India, and Southeast Asia to deploy AI research assistants with minimal setup. By using globally accessible tools like FastAPI, React, and Gemini APIs, the project is well-positioned for widespread adoption. Key Takeaways Agent Design: Modular React + LangGraph system supports autonomous query generation and reflection. Iterative Reasoning: Agent refines search queries until confidence thresholds are met. Citations Built-In: Outputs include direct links to web sources for transparency. Developer-Ready: Local setup requires Node.js, Python 3.8+, and a Gemini API key. Open-Source: Publicly available for community contribution and extension. Conclusion By combining Google’s Gemini 2.5 with LangGraph’s logic orchestration, this project delivers a breakthrough in autonomous AI reasoning. It showcases how research workflows can be automated without compromising accuracy or traceability. As conversational agents evolve, systems like this one set the standard for intelligent, trustworthy, and developer-friendly AI research tools. Check out the GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 99k+ ML SubReddit and Subscribe to our Newsletter. The post Google Introduces Open-Source Full-Stack AI Agent Stack Using Gemini 2.5 and LangGraph for Multi-Step Web Search, Reflection, and Synthesis appeared first on MarkTechPost.

Large Language Models (LLMs) generate step-by-step responses known as Chain-of-Thoughts (CoTs), where each token contributes to a coherent and logical narrative. To improve the quality of reasoning, various reinforcement learning techniques have been employed. These methods allow the model to learn from feedback mechanisms by aligning generated outputs with correctness criteria. As LLMs grow in complexity and capacity, researchers have begun probing the internal structure of token generation to discern patterns that enhance or limit performance. One area gaining attention is the token entropy distribution, a measurement of uncertainty in token prediction, which is now being linked to the model’s ability to make meaningful logical decisions during reasoning. A core issue in training reasoning models using reinforcement learning is treating all output tokens equally. When models are optimized using reinforcement learning with verifiable rewards (RLVR), the update process traditionally includes every token in the generated sequence, regardless of its functional role. This uniform treatment fails to distinguish tokens that lead to significant reasoning shifts from those that merely extend existing linguistic structures. As a result, a large portion of training resources may be directed at tokens that offer minimal contribution to the model’s reasoning capabilities. Without prioritizing the few tokens that play decisive roles in navigating different logic paths, these methods miss opportunities for focused and effective optimization. Most RLVR frameworks, including Proximal Policy Optimization (PPO), Group Relative Policy Optimization (GRPO), and Dynamic sAmpling Policy Optimization (DAPO), function by evaluating entire sequences of token outputs against reward functions that assess correctness. PPO relies on stabilizing policy updates through a clipped objective function. GRPO improves upon this by estimating advantage values using grouped responses, rather than a separate value network. DAPO introduces additional enhancements, such as the clip-higher mechanism and overlong reward shaping. These methods, however, do not factor in token-level entropy or distinguish the importance of individual tokens in the reasoning chain, instead applying uniform gradient updates across the board. In an attempt to refine how RLVR training impacts LLM reasoning, researchers from Alibaba Inc. and Tsinghua University presented a new methodology focused on token entropy patterns. They observed that in the CoT sequences generated by Qwen3 models, a small subset of tokens, roughly 20%, display significantly higher entropy. These tokens, labeled “forking tokens,” often correspond to moments where the model must decide between multiple reasoning paths. The remaining 80% of tokens typically exhibit low entropy and act as extensions of prior statements. By limiting policy gradient updates solely to these high-entropy tokens, the research team was able not only to maintain but, in many cases, improve performance on challenging reasoning benchmarks. To quantify token entropy, the researchers used the entropy formula based on the probability distribution over possible token choices at each step. They found that over half of all generated tokens had entropy values below 0.01, indicating near-deterministic behavior. Only 20% exceeded an entropy of 0.672, marking them as the decision-making hubs within CoTs. High-entropy tokens often include logical operators and connective words such as “assume,” “since,” or “thus,” which introduce new conditions or transitions in logic. In contrast, low-entropy tokens included predictable symbols, suffixes, or code fragments. Through controlled experiments, it became clear that manipulating the entropy of these forking tokens directly influenced the model’s reasoning performance, while altering low-entropy tokens had little effect. The research team conducted extensive experiments across three model sizes: Qwen3-8B, Qwen3-14B, and Qwen3-32B. When training only the top 20% high-entropy tokens, the Qwen3-32B model achieved a score of 63.5 on AIME’24 and 56.7 on AIME’25, both setting new performance benchmarks for models under 600B parameters. Furthermore, increasing the maximum response length from 20k to 29k raised the AIME’24 score to 68.1. In comparison, training on the bottom 80% of low-entropy tokens caused performance to drop significantly. The Qwen3-14B model showed gains of +4.79 on AIME’25 and +5.21 on AIME’24, while the Qwen3-8B maintained competitive results relative to full-token training. An ablation study further confirmed the importance of retaining the 20% threshold. Decreasing the fraction to 10% omitted essential decision points, and increasing it to 50% or 100% diluted the effect by including too many low-entropy tokens, thereby reducing entropy diversity and hindering exploration. In essence, the research provides a new direction for enhancing the reasoning abilities of language models by identifying and selectively training on the minority of tokens that disproportionately contribute to reasoning success. It avoids inefficient training and instead proposes a scalable approach that aligns reinforcement learning objectives with actual decision-making moments in token sequences. The success of this strategy lies in using entropy as a guide to distinguish useful tokens from filler. Several Key takeaways from the research include: Around 20% of tokens exhibit high entropy and serve as forking points that direct reasoning paths. Training only on these high-entropy tokens delivers performance equal to or better than training on the full token set. Qwen3-32B achieved scores of 63.5 on AIME’24 and 56.7 on AIME’25, outperforming larger models trained traditionally. Extending response length from 20k to 29k further pushed the AIME’24 score to 68.1. Training on the remaining 80% of low-entropy tokens led to sharp performance degradation. Retaining the 20% threshold for high-entropy tokens optimally balances exploration and performance. Larger models gain more from this strategy due to their capacity to benefit from enhanced exploration. The strategy scales well and could guide more efficient training of next-generation reasoning models. In conclusion, this research effectively rethinks the application of reinforcement learning to language models by introducing a focus on token-level entropy. By optimizing only the minority that influences reasoning paths, the method enhances performance while reducing computational overhead. It provides a practical roadmap for future efforts to improve reasoning in LLMs without unnecessary complexity. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 98k+ ML SubReddit and Subscribe to our Newsletter. The post High-Entropy Token Selection in Reinforcement Learning with Verifiable Rewards (RLVR) Improves Accuracy and Reduces Training Cost for LLMs appeared first on MarkTechPost.

AI agents are moving from passive assistants to active participants. Today, we ask them to do. Tomorrow, we’ll authorize them to act.Read More

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing for high levels of control during the generation process. As researchers began to apply these techniques to visual data, they found that structured prediction not only preserved spatial integrity but also supported tasks like image manipulation and multimodal translation effectively. Despite these benefits, generating high-resolution images remains computationally expensive and slow. A primary issue is the number of tokens needed to represent complex visuals. Raster-scan methods that flatten 2D images into linear sequences require thousands of tokens for detailed images, resulting in long inference times and high memory consumption. Models like Infinity need over 10,000 tokens for a 1024×1024 image. This becomes unsustainable for real-time applications or when scaling to more extensive datasets. Reducing the token burden while preserving or improving output quality has become a pressing challenge. Efforts to mitigate token inflation have led to innovations like next-scale prediction seen in VAR and FlexVAR. These models create images by predicting progressively finer scales, which imitates the human tendency to sketch rough outlines before adding detail. However, they still rely on hundreds of tokens—680 in the case of VAR and FlexVAR for 256×256 images. Moreover, approaches like TiTok and FlexTok use 1D tokenization to compress spatial redundancy, but they often fail to scale efficiently. For example, FlexTok’s gFID increases from 1.9 at 32 tokens to 2.5 at 256 tokens, highlighting a degradation in output quality as the token count grows. Researchers from ByteDance introduced DetailFlow, a 1D autoregressive image generation framework. This method arranges token sequences from global to fine detail using a process called next-detail prediction. Unlike traditional 2D raster-scan or scale-based techniques, DetailFlow employs a 1D tokenizer trained on progressively degraded images. This design allows the model to prioritize foundational image structures before refining visual details. By mapping tokens directly to resolution levels, DetailFlow significantly reduces token requirements, enabling images to be generated in a semantically ordered, coarse-to-fine manner. The mechanism in DetailFlow centers on a 1D latent space where each token contributes incrementally more detail. Earlier tokens encode global features, while later tokens refine specific visual aspects. To train this, the researchers created a resolution mapping function that links token count to target resolution. During training, the model is exposed to images of varying quality levels and learns to predict progressively higher-resolution outputs as more tokens are introduced. It also implements parallel token prediction by grouping sequences and predicting entire sets at once. Since parallel prediction can introduce sampling errors, a self-correction mechanism was integrated. This system perturbs certain tokens during training and teaches subsequent tokens to compensate, ensuring that final images maintain structural and visual integrity. The results from the experiments on the ImageNet 256×256 benchmark were noteworthy. DetailFlow achieved a gFID score of 2.96 using only 128 tokens, outperforming VAR at 3.3 and FlexVAR at 3.05, both of which used 680 tokens. Even more impressive, DetailFlow-64 reached a gFID of 2.62 using 512 tokens. In terms of speed, it delivered nearly double the inference rate of VAR and FlexVAR. A further ablation study confirmed that the self-correction training and semantic ordering of tokens substantially improved output quality. For example, enabling self-correction dropped the gFID from 4.11 to 3.68 in one setting. These metrics demonstrate both higher quality and faster generation compared to established models. By focusing on semantic structure and reducing redundancy, DetailFlow presents a viable solution to long-standing issues in autoregressive image generation. The method’s coarse-to-fine approach, efficient parallel decoding, and ability to self-correct highlight how architectural innovations can address performance and scalability limitations. Through their structured use of 1D tokens, the researchers from ByteDance have demonstrated a model that maintains high image fidelity while significantly reducing computational load, making it a valuable addition to image synthesis research. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. The post ByteDance Researchers Introduce DetailFlow: A 1D Coarse-to-Fine Autoregressive Framework for Faster, Token-Efficient Image Generation appeared first on MarkTechPost.

Multi-agent systems are becoming a critical development in artificial intelligence due to their ability to coordinate multiple large language models (LLMs) to solve complex problems. Instead of relying on a single model’s perspective, these systems distribute roles among agents, each contributing a unique function. This division of labor enhances the system’s ability to analyze, respond, and act in more robust ways. Whether applied to code debugging, data analysis, retrieval-augmented generation, or interactive decision-making, LLM-driven agents are achieving results that single models cannot consistently match. The power of these systems lies in their design, particularly the configuration of inter-agent connections, known as topologies, and the specific instructions given to each agent, referred to as prompts. As this model of computation matures, the challenge has shifted from proving feasibility to optimizing architecture and behavior for superior results. One significant problem lies in the difficulty of designing these systems efficiently. When prompts, those structured inputs that guide each agent’s role, are slightly altered, performance can swing dramatically. This sensitivity makes scalability risky, especially when agents are linked together in workflows where one’s output serves as another’s input. Errors can propagate or even amplify. Moreover, topological decisions, such as determining the number of agents involved, their interaction style, and task sequence, are still heavily reliant on manual configuration and trial-and-error. The design space is vast and nonlinear, as it combines numerous options for both prompt engineering and topology construction. Optimizing both simultaneously has been largely out of reach for traditional design methods. Several efforts have been made to improve various aspects of this design problem, but gaps remain. Methods like DSPy automate exemplar generation for prompts, while others focus on increasing the number of agents participating in tasks like voting. Tools like ADAS introduce code-based topological configurations through meta-agents. Some frameworks, such as AFlow, apply techniques like Monte Carlo Tree Search to explore combinations more efficiently. Yet, these solutions generally concentrate on either prompt or topology optimization, rather than both. This lack of integration limits their ability to generate MAS designs that are both intelligent and robust under complex operational conditions. Researchers at Google and the University of Cambridge introduced a new framework named Multi-Agent System Search (Mass). This method automates MAS design by interleaving the optimization of both prompts and topologies in a staged approach. Unlike earlier attempts that treated the two components independently, Mass begins by identifying which elements, both prompts and topological structures, are most likely to influence performance. By narrowing the search to this influential subspace, the framework operates more efficiently while delivering higher-quality outcomes. The method progresses in three phases: localized prompt optimization, selection of effective workflow topologies based on the optimized prompts, and then global optimization of prompts at the system-wide level. The framework not only reduces computational overhead but also removes the burden of manual tuning from researchers. The technical implementation of Mass is structured and methodical. First, each building block of a MAS undergoes prompt refinement. These blocks are agent modules with specific responsibilities, such as aggregation, reflection, or debate. For example, prompt optimizers generate variations that include both instructional guidance (e.g., “think step by step”) and example-based learning (e.g., one-shot or few-shot demos). The optimizer evaluates these using a validation metric to guide improvements. Once each agent’s prompt is optimized locally, the system proceeds to explore valid combinations of agents to form topologies. This topology optimization is informed by earlier results and constrained to a pruned search space identified as most influential. Finally, the best topology undergoes global-level prompt tuning, where instructions are fine-tuned in the context of the entire workflow to maximize collective efficiency. In tasks such as reasoning, multi-hop understanding, and code generation, the optimized MAS consistently surpassed existing benchmarks. In performance testing using Gemini 1.5 Pro on the MATH dataset, prompt-optimized agents showed an average accuracy of around 84% with enhanced prompting techniques, compared to 76–80% for agents scaled through self-consistency or multi-agent debate. In the HotpotQA benchmark, using the debate topology within Mass yielded a 3% improvement. In contrast, other topologies, such as reflect or summarize, failed to yield gains or even led to a 15% degradation. On LiveCodeBench, the Executor topology provided a +6% boost, but methods like reflection again saw negative results. These findings validate that only a fraction of the topological design space contributes positively and reinforce the need for targeted optimization, such as that used in Mass. Several Key Takeaways from the Research include: MAS design complexity is significantly influenced by prompt sensitivity and topological arrangement. Prompt optimization, both at the block and system level, is more effective than agent scaling alone, as evidenced by the 84% accuracy with enhanced prompts versus 76% with self-consistency scaling. Not all topologies are beneficial; debate added +3% in HotpotQA, while reflection caused a drop of up to -15%. The Mass framework integrates prompt and topology optimization in three phases, drastically reducing computational and design burden. Topologies like debate and executor are effective, while others, such as reflect and summarize, can degrade system performance. Mass avoids full search complexity by pruning the design space based on early influence analysis, improving performance while saving resources. The approach is modular and supports plug-and-play agent configurations, making it adaptable to various domains and tasks. Final MAS models from Mass outperform state-of-the-art baselines across multiple benchmarks like MATH, HotpotQA, and LiveCodeBench. In conclusion, this research identifies prompt sensitivity and topology complexity as major bottlenecks in multi-agent system (MAS) development and proposes a structured solution that strategically optimizes both areas. The Mass framework demonstrates a scalable, efficient approach to MAS design, minimizing the need for human input while maximizing performance. The research presents compelling evidence that better prompt design is more effective than merely adding agents and that targeted search within influential topology subsets leads to meaningful gains in real-world tasks. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. The post Google AI

This week, I’ve been thinking about the powerful connection between mind and body. Some new research suggests that people with heart conditions have better outcomes when they are more hopeful and optimistic. Hopelessness, on the other hand, is associated with a significantly higher risk of death. The findings build upon decades of fascinating research into the phenomenon of the placebo effect. Our beliefs and expectations about a medicine (or a sham treatment) can change the way it works. The placebo effect’s “evil twin,” the nocebo effect, is just as powerful—negative thinking has been linked to real symptoms. Researchers are still trying to understand the connection between body and mind, and how our thoughts can influence our physiology. In the meantime, many are developing ways to harness it in hospital settings. Is it possible for a doctor to prescribe hope? Alexander Montasem, a lecturer in psychology at the University of Liverpool, is trying to find an answer to that question. In his latest study, Montasem and his colleagues focused on people with cardiovascular disease. The team reviewed all published research into the link between hope and heart health outcomes in such individuals. Hope is a pretty tricky thing to nail down, but these studies use questionnaires to try to do that. In one popular questionnaire, hope is defined as “a positive motivational state” based on having agency and plans to meet personal goals. Montasem’s team found 12 studies that fit the bill. All told, these studies included over 5,000 people. And together, they found that high hopefulness was associated with better health outcomes: less angina, less post-stroke fatigue, a higher quality of life, and a lower risk of death. The team presented its work at the British Cardiovascular Society meeting in Manchester earlier this week. When I read the results, it immediately got me thinking about the placebo effect. A placebo is a “sham” treatment—an inert substance like a sugar pill or saline injection that does not contain any medicine. And yet hundreds of studies have shown that such treatments can have remarkable effects. They can ease the symptoms of pain, migraine, Parkinson’s disease, depression, anxiety, and a host of other disorders. The way a placebo is delivered can influence its effectiveness, and so can its color, shape, and price. Expensive placebos seem to be more effective. And placebos can even work when people know they are just placebos. And then there’s the nocebo effect. If you expect to feel worse after taking something, you are much more likely to. The nocebo effect can increase the risk of pain, gastrointestinal symptoms, flu-like symptoms, and more.   It’s obvious our thoughts and beliefs can play an enormous role in our health and well-being. What’s less clear is exactly how it happens. Scientists have made some progress—there’s evidence that a range of brain chemicals, including the body’s own opioids, are involved in both the placebo and nocebo effects. But the exact mechanisms remain something of a mystery. In the meantime, researchers are working on ways to harness the power of positive thinking. There have been long-running debates over whether it is ever ethical for a doctor to deceive patients to make them feel better. But I’m firmly of the belief that doctors have a duty to be honest with their patients. A more ethical approach might be to find ways to build patients’ hope, says Montasem. Not by exaggerating the likely benefit of a drug or by sugar-coating a prognosis, but perhaps by helping them work on their goals, agency, and general outlook on life. Some early research suggests that this approach can help. Laurie McLouth at the University of Kentucky and her colleagues found that a series of discussions about values, goals, and strategies to achieve those goals improved hope among people being treated for advanced lung cancer. Montasem now plans to review all the published work in this area and design a new approach to increasing hope. Any approach might have to be tailored to an individual, he adds. Some people might be more responsive to a more spiritual or religious way of thinking about their lives, for example. These approaches could also be helpful for all of us, even outside clinical settings. I asked Montasem if he had any advice for people who want to have a positive outlook on life more generally. He told me that it’s important to have personal goals, along with a plan to achieve them. His own goals center on advancing his research, helping patients, and spending time with his family. “Materialistic goals aren’t as beneficial for your wellbeing,” he adds. Since we spoke, I’ve been thinking over my own goals. I’ve realized that my first is to come up with a list of goals. And I plan to do it soon. “The minute we give up [on pursuing] our goals, we start falling into hopelessness,” he says. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Missing values appear more often than not in many real-world datasets.