AI Agents Embrace Self-Doubt: ExpSeek Framework Enables Robots to Actively Seek Help Through Collaboration Between CAS and Alibaba

AI Agents Experience “Self-Doubt”: ExpSeek Framework Developed by the Chinese Academy of Sciences and Alibaba Teaches Robots to Seek Help Actively

This research, conducted collaboratively by the Institute of Information Engineering at the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and Alibaba’s Tongyi Laboratory, introduces the ExpSeek framework. It empowers AI agents with “self-awareness,” allowing them to proactively seek guidance based on their internal level of confusion. The system determines when to seek help by monitoring entropy values and provides personalized suggestions from a structured experience database, achieving a significant performance increase of 7.5% to 9.3% across various benchmark tests. This paves the way for the development of more intelligent and adaptive AI systems.

The study, published on January 13, 2026, on the arXiv preprint platform with the paper ID arXiv:2601.08605v1, offers detailed technical insights for those interested. Readers can refer to this ID to access the full research paper.

Imagine how you would react when faced with a challenging problem. You might pause to reflect, realize you need assistance, and then actively seek advice from friends or experts. However, current AI agents typically do not behave this way. They are often overloaded with various experiences and knowledge before they even begin their tasks, much like students burdened with heavy backpacks, forced to rummage through their belongings for answers regardless of the situation.

This traditional approach has a fundamental flaw: when AI agents interact with complex network environments, the circumstances are constantly changing, yet they lack the flexibility to seek help based on their current level of confusion. It is akin to a person lost in an unfamiliar city who fails to ask for directions based on their confusion level, instead carrying around a pile of potentially useless maps and guides.

The research team identified the core issue: existing AI agents lack “self-awareness.” They do not recognize when they should pause to seek assistance or what type of help they require. In response, the researchers developed an innovative framework called ExpSeek, which equips AI agents with human-like “self-doubt” and “proactive help-seeking” capabilities.

The key innovation of ExpSeek lies in enabling AI agents to perceive their internal states. When they feel confused or uncertain, they actively seek relevant experiential guidance. This is like equipping AI agents with a “confusion sensor.” When this sensor indicates high levels of confusion, the agent will consult the “experience database” to receive targeted advice.

The research team validated the effectiveness of ExpSeek across four challenging network agent benchmark tests using different sizes of the Qwen3 model (8B and 32B parameters). The results were impressive: ExpSeek led to an absolute performance increase of 9.3% and 7.5%, respectively. Surprisingly, even a small experience model with only 4B parameters significantly improved the performance of the larger 32B model, demonstrating the feasibility of “weaker guiding stronger” in the AI field.

The significance of this research extends beyond numerical performance improvements. It represents a shift in AI agents’ thinking from “passively receiving” to “actively seeking,” paving new paths for building smarter and more adaptive AI systems. When AI agents learn self-reflection and proactive help-seeking, they can perform better in the complex and ever-changing real world.

1. The Growing Pains of AI Agents: Why Learning to Seek Help is Necessary

Throughout the development of artificial intelligence, enabling machines to learn from and apply experiences has been a critical goal. Just as humans accumulate life experiences to tackle various challenges, AI researchers hope that agents can learn from past successes and failures to perform better when faced with new problems.

Current mainstream approaches can be likened to two different learning styles. The first is akin to “cramming” before an exam, where researchers compile a large number of success and failure cases into reusable experience patterns and provide these as “reference materials” before the AI agent undertakes a task. The second method resembles “learning by doing,” where agents accumulate experiences through interactions with their environments, improving their performance via trial and feedback.

However, both methods share a common limitation: they overwhelm the agent with experiences before the task begins, akin to handing a student a thick reference book and expecting them to find the most relevant information during the exam. The problem with this approach is that as agents engage in multiple rounds of interaction with their environments, situations evolve, and pre-provided experiences often do not perfectly match the current context.

This issue is particularly pronounced in the application of network agents. The network environment itself is a complex system filled with vast amounts of information that are constantly changing and often noisy or incomplete. When an agent needs to search for information online, visit web pages, or analyze content, every step’s outcome can influence the next decision. In such a dynamic environment, the fixed method of injecting experiences becomes insufficient.

The research team recognized that a true solution should endow agents with an intuition-like ability similar to humans: knowing when they encounter difficulties and need to seek help. When a person feels confused while addressing a complex problem, they naturally pause to reflect, assess their understanding, and decide whether to seek external assistance. If help is needed, they also consider what type of assistance would be most appropriate.

ExpSeek emerged from these observations. It no longer overwhelms agents with all experiences at the start of a task. Instead, it allows agents to self-assess at every step of execution and proactively seek the most relevant experiential guidance when experiencing confusion. This approach aligns more closely with human cognitive patterns and is better suited to dynamically changing task environments.

This transition signifies more than just technological improvement; it represents the evolution of AI agents from “passive executors” to “active learners.” When agents learn to self-monitor and seek help proactively, they can respond more flexibly and intelligently to unknown challenges.

2. Core Innovation of ExpSeek: Teaching AI to “Read the Room”

The greatest innovation of the ExpSeek framework addresses two critical questions: how do agents know they need help, and what type of help do they require? This is akin to teaching a person to have self-awareness and know whom to ask for assistance.

To tackle the first question, the research team employed a clever method: they used the model’s own “entropy” as an indicator of confusion. In information theory, entropy represents the degree of uncertainty. The higher the uncertainty in the AI model’s outputs, the greater the entropy. This is similar to a person’s hesitation when answering a question; if they are confident in their answer, they will respond smoothly. If they are uncertain, they will appear hesitant.

The researchers validated this hypothesis through extensive experimental data. They collected performance data from agents handling various tasks, labeling each operation as either “correct” or “incorrect,” and analyzed the findings: entropy values for correct steps are typically lower, while incorrect steps show higher entropy. This regularity provides scientific evidence for using entropy as a “self-awareness” signal.

However, this distinguishing effect varies among different types of steps. In cognitive steps during the process, the entropy values for correct and incorrect actions overlap to some extent because agents may display high uncertainty even when exploring the right direction. However, in the final answering step, this distinction becomes quite pronounced, with the entropy of correct answers significantly lower than that of incorrect ones.

Based on this discovery, the research team designed a dynamic threshold system. They employed statistical techniques, including logistic regression and bootstrapping methods, to calculate corresponding entropy threshold intervals for different types of steps. When an agent’s entropy falls within this threshold range, the system probabilistically triggers the help-seeking mechanism. This probabilistic triggering method ensures that help is obtained when genuinely needed while avoiding excessive interference.

Regarding the second question—what type of help is needed—ExpSeek constructed a structured experience database. This database does not simply store success cases; instead, it organizes each experience into a triplet: describing the current action, analyzing the cause of errors, and providing improvement suggestions. This is akin to preparing a complete “diagnosis and treatment” plan for each common error.

The construction process of the experience database itself is intriguing. The system collects a large number of success and failure cases, allowing a specialized analysis model to compare these cases and identify key error points within failure trajectories. For each error point, the analysis model objectively describes the behavior state at that time, delves into the root causes of the errors, and then offers specific improvement suggestions based on successful cases.

To facilitate better organization and retrieval of these experiences, the system automatically categorizes experiences with thematic tags. This process employs an iterative theme generation strategy: whenever a new batch of experiences is processed, the system assesses whether they can fit into existing themes, if new theme categories need to be created, or if current categories require adjustments. This results in a continuously refined experience classification system.

When the agent’s self-monitoring mechanism triggers a help request, a specialized experience model intervenes. This model analyzes the current task context, selects the most relevant topic categories from the experience database, and generates targeted guidance suggestions based on these experiences and the specific circumstances at hand.

The brilliance of this design lies in its realization of “personalized education.” The guidance content is not a fixed template written in advance but dynamically generated individualized suggestions tailored to the current situation. This is akin to an experienced mentor who not only understands various common problem-solving approaches but also provides the most suitable advice based on the specific context of the student.

3. Wisdom Accumulation of Experience Database: The Art of Learning from Failures

The design of ExpSeek’s experience database embodies the profound wisdom of “learning from failures.” Unlike traditional methods that merely collect success cases, this system focuses on behaviors that may seem correct but ultimately lead to incorrect results. This design philosophy creates a “museum of errors,” allowing future users to gain valuable insights from past mistakes.

The construction process of the experience database can be likened to meticulous case analysis. The research team first allowed AI agents to attempt multiple solutions on training data; each problem would yield various paths. Some paths lead to correct answers, while others end in failure. The critical insight is that even failed paths may contain reasonable steps, with issues often arising at specific key junctions.

To precisely identify these key junctions, the system employs comparative analysis. It systematically contrasts successful and failing trajectories, pinpointing the critical points where they diverge and analyzing what actions were taken along the failing path and why those actions ultimately resulted in errors. This process resembles accident investigators analyzing traffic accidents, aiming not only to identify immediate causes but also to understand the entire sequence of events.

Every identified error pattern is organized into a standardized triplet format. The first part, “behavior description,” employs objective and neutral language to report the situation and actions taken by the agent at that time, devoid of any value judgment. The second part, “error analysis,” delves into why that behavior led to failure, often involving misunderstandings of task requirements, incorrect processing of information, or deviations in strategies. The third part, “improvement suggestions,” draws from successful case experiences to offer concrete, actionable directions.

This structured organization of experiences possesses an essential characteristic: it does not directly tell agents what the answer is but instead points to the direction of thought and traps to avoid. This is akin to a good teacher who does not do the homework for students but indicates problem-solving approaches and common pitfalls.

The process of theme classification for experiences reveals another layer of wisdom. The system does not simply classify experiences based on superficial characteristics; it deeply categorizes them based on the fundamental nature of the errors. For instance, a typical error pattern might be “ignoring highly relevant evidence,” while another could be “misjudgment of information.” This classification approach enables experiences to transcend specific task boundaries, providing guidance in different but fundamentally similar situations.

The theme generation employs a dynamic adjustment strategy. When processing new experiences, the system evaluates whether they can be classified into existing themes. If existing categories adequately cover the features of the new experiences, they will be classified accordingly. If the existing categories are inaccurate, the system will adjust their definitions to be more inclusive. If new experiences represent entirely different error patterns, the system will create new theme categories. This adaptive classification mechanism ensures the experience database continually improves with use.

It is noteworthy that the experience database maintains different sub-databases for various types of steps. The experience for processing steps primarily focuses on strategic and methodological guidance, such as how to choose information sources and validate their reliability. Meanwhile, the experiences related to final answering steps emphasize detail and accuracy, such as how to extract complete answers from search results and avoid missing key information.

This refined organization of experiences allows ExpSeek to provide timely assistance. When agents encounter difficulties in strategizing, they receive guidance on methods and directions; when they feel uncertain about formulating the final answer, they receive suggestions regarding detail handling and accuracy checks.

4. Dynamic Guidance Mechanism: Tailored Real-Time Assistance

ExpSeek’s dynamic guidance mechanism exemplifies the dual wisdom of “timely adaptation” and “personalized education.” This mechanism does not simply retrieve ready-made answers from the experience database; instead, it acts like an experienced mentor, providing personalized guidance suggestions based on the current specific difficulties and progress of the learner.

When the agent’s self-monitoring system detects a need for assistance, a specialized experience model takes over the guidance task. The working process of this model can be likened to the diagnostic process of a consulting expert. First, it carefully analyzes the current context in which the agent finds itself, including completed steps, current challenges, and overall task requirements. Then, it identifies the three most relevant topic categories from the experience database, similar to how a doctor preliminarily classifies possible causes based on symptoms.

The theme selection process reflects a deep semantic understanding capability. The experience model does not merely perform keyword matching; it comprehends the essential features of the current context and identifies the most fitting types of error patterns. For example, when an agent may have overlooked crucial information during a web search, the system will automatically activate the experience theme related to “incomplete information collection”; when the agent’s understanding of search results deviates, the system will call upon guidance experiences related to “misinterpretation of information.”

Once relevant themes are confirmed, the experience model enters the guidance content generation phase. The brilliance of this phase lies in its approach: it does not simply copy and paste existing experience content but instead combines these experiences with the current specific context to generate targeted, personalized suggestions. This mirrors an experienced teacher who, although familiar with many similar problems, tailors their guidance to the specific situation of each student.

The expression of guidance content is also meticulously designed. It does not directly tell the agent what to do nor divulge the final answer; instead, it employs heuristic expressions to guide agents toward the correct direction. For instance, it would not say “the answer is X,” but rather, “you may need to check more thoroughly whether the previously searched information contains data of type Y” or “consider revisiting the specific limitations regarding Z in the task requirements.”

The timing of guidance delivery also reflects the system’s intelligence level. For guidance during processing steps, the system attaches suggestions after environmental feedback, allowing the agent to see the results of tool execution while also viewing related suggestions. This can help the agent better understand the current situation and plan subsequent actions. For guidance during the final answering steps, feedback is provided immediately after the agent delivers an answer, giving the agent the opportunity to reconsider and refine their response.

To avoid excessive intervention, ExpSeek has designed a crucial protective mechanism: after providing guidance, the system pauses for the next step, allowing the agent ample time and space to digest and apply the received suggestions. This design prevents the issue of “over-guiding,” ensuring that agents can autonomously develop problem-solving capabilities under guidance.

Another important feature of dynamic guidance is its adaptability. The experience model considers the agent’s current state and historical performance when generating guidance, avoiding repetitive recommendations of the same type. If the agent has already performed well in a particular area, the system shifts focus to other potentially needing improvement areas. This adaptive guidance strategy ensures that every intervention yields tangible benefits.

The design philosophy of the entire dynamic guidance process is “teaching to fish rather than giving fish.” The system’s goal is not to solve problems for the agents but to help them develop better problem-solving abilities. Through this approach, agents can not only perform better in current tasks but also learn transferable strategies from each guidance experience, enabling them to excel in future challenges.

5. Experimental Verification: Breakthrough Discoveries Behind the Numbers

The research team validated the efficacy of ExpSeek across four challenging real-world network agent benchmark tests, each representing different types of complex task challenges.

The first testing platform is GAIA, specifically designed to evaluate the abilities of general AI assistants. Questions on GAIA typically require agents to engage in multi-step reasoning and network searches, akin to completing a comprehensive research report. The second test, WebWalkerQA, assesses agents’ navigation and information extraction capabilities within network environments. The third test, xbench-DeepSearch, focuses on deep searching and information integration abilities. Lastly, SEAL-HARD, as its name suggests, tests agents’ performance in complex search-enhanced tasks.

The experiments used two different sizes of the Qwen3 model as the base agents: the 8B parameter version and the 32B parameter version. The differences between these models resemble the distinction between high school and university students; while the smaller model is agile but has limited capabilities, the larger model possesses a wider knowledge base but may “overthink” in certain situations.

The experimental results were striking. The 8B model showed an average absolute performance increase of 9.3% with ExpSeek. Although this number might seem small, it is a considerable improvement in AI performance evaluation. Even more impressively, in specific tasks, the performance improvement reached 14.6%, meaning that an agent that could initially complete only half the tasks can now succeed in over 60% of them.

Results for the 32B model were equally encouraging, with an average increase of 7.5%. While the absolute figures are slightly lower than those of the 8B model, considering that the benchmark performance of larger models is already higher, this increase actually represents greater practical value. It’s akin to helping an already excellent student improve their score by a few additional points, which is more challenging than assisting an average student to achieve the same score increase.

Interestingly, differences in performance emerged across different task types. ExpSeek’s effects were particularly evident in tasks requiring extensive information collection and integration. For example, in the difficulty level test of WebWalkerQA, the success rate for the 8B model rose from 32.56% to 44.22%, an increase of more than 11 percentage points. This indicates that as tasks become more complex, agents increasingly require timely experiential guidance.

The research team also conducted a particularly intriguing experiment regarding “weaker guiding stronger.” They used a small model with only 4B parameters as the experience guiding model to assist the larger 32B model. The results showed that even though the experience model’s “intellectual level” was significantly lower than that of the model being guided, it still provided a performance improvement ranging from 5.2% to 9.7%. This finding challenges the stereotype that “bigger is stronger” in AI models, demonstrating that under the right framework, smaller models can also offer unique value.

During the experiments, an interesting phenomenon was observed: ExpSeek altered the “thinking patterns” of the agents. By analyzing the changes in entropy distribution at each step, researchers discovered that agents guided by ExpSeek exhibited greater exploratory behavior (increased entropy) during the process but displayed higher certainty (decreased entropy) when providing final answers. This “divergent-then-convergent” pattern closely aligns with human cognitive processes in solving complex problems.

Comparative experiments further confirmed the superiority of the ExpSeek method. The research team compared ExpSeek with two mainstream experience utilization methods: Training-Free GRPO and ReasoningBank+. Both of these approaches rely on traditional global experience injection methods, providing all relevant experiences to the agent at the start of the task. The results indicated that the improvements from these traditional methods were minimal, sometimes even resulting in negative impacts, while ExpSeek consistently demonstrated significant enhancements across all tests.

The research team also tested the generalization abilities of ExpSeek. Although the experience database was entirely constructed from the WebWalkerQA dataset, ExpSeek maintained good performance improvements across three other completely different test sets. This suggests that what ExpSeek learned are not specific task skills but rather more universal problem-solving strategies.

6. In-Depth Mechanism Analysis: The “Self-Evolution” Process of AI Agents

The working mechanism of ExpSeek is more profound and sophisticated than it appears. To understand how this system genuinely alters the behavior patterns of AI agents, the research team conducted an in-depth mechanism analysis, akin to a doctor performing various checks to comprehend how a treatment plan operates within a patient.

The most striking discovery was the fundamental transformation of the agents’ “thinking patterns.” By analyzing agents’ behavior patterns before and after receiving ExpSeek guidance, researchers identified a “divergent-convergent” process similar to human cognition. In traditional methods, agents often exhibited relatively rigid thinking patterns, either maintaining low exploration and easily falling into local optima or remaining highly uncertain and struggling to form clear conclusions.

In contrast, agents guided by ExpSeek displayed a more mature cognitive pattern. During the initial and middle stages of problem-solving, they exhibited higher exploratory behavior, willing to try various different paths, reflected in increased entropy values. However, as they approached the final answer, they showed stronger certainty and convergence, indicated by significantly lower entropy values. This pattern aligns closely with how human experts tackle complex problems: broadly exploring possibilities first and then focusing on the optimal solution.

To validate the effectiveness of entropy as a self-trigger signal, the research team designed various comparative experiments. They tested rule-based triggering mechanisms (e.g., providing guidance every fixed number of steps) and mechanisms based on external model judgments (using another AI model to decide if guidance is needed). The results indicated that these alternatives either led to over-intervention, causing inefficiencies, or inaccurate judgments resulting in poor outcomes. Only the self-entropy-based triggering mechanism could provide accurate interventions while maintaining efficiency.

The research also revealed an important “adaptive learning” phenomenon. As task difficulty increases, the triggering frequency of ExpSeek automatically adjusts. In relatively simple tasks, agents rarely trigger the help-seeking mechanism, mainly relying on their own capabilities to complete tasks. However, in complex tasks, the triggering frequency significantly increases, ensuring that agents receive necessary guidance at critical moments. This adaptability demonstrates that ExpSeek truly achieves “on-demand guidance” rather than mechanical fixed interventions.

Deeper analyses showed that ExpSeek altered the agents’ “memory utilization patterns.” In traditional methods, agents generally only consult the provided experience information at the task’s outset, primarily relying on this initial information throughout execution. ExpSeek, however, created a “dynamic memory invocation” pattern, enabling agents to dynamically access the most relevant experiential fragments according to their current needs during execution. This resembles a shift from “one-time reading of reference materials” to “consulting a specialized dictionary at any time.”

The research team also discovered patterns regarding the impact of experience database size on performance. Surprisingly, even when reducing the experience database to retain only one example per theme, ExpSeek still delivered significant performance improvements. This indicates that the system’s value lies not in the quantity of experiences but in their quality and timing of use. This finding holds significant implications for practical applications, as it suggests that ExpSeek can achieve good results with relatively few high-quality experience data.

Another intriguing discovery concerns the feasibility of “cross-model experience transfer.” When researchers applied the experience database constructed for the 8B model to the 32B model, while the effects decreased, a considerable degree of improvement remained. This suggests that the experience patterns captured by ExpSeek possess a degree of universality, not completely dependent on the specific features of the models.

Through the analysis of numerous interaction trajectories, researchers found that ExpSeek enhanced agents’ “problem diagnosis abilities.” In the absence of guidance, agents often repeated the same or similar strategies when encountering difficulties. In contrast, with the assistance of ExpSeek, agents learned to better identify the essence of problems and adjust their solving strategies accordingly. This change not only improved success rates but also significantly reduced the average number of steps required to resolve issues.

7. Technical Details Revealed: Engineering Wisdom in Building Intelligent Guidance Systems

The implementation of the ExpSeek system involves multiple innovative technical designs, with every detail reflecting the research team’s careful consideration in engineering practice. Although these technical details may seem complex, they all serve a common goal: to endow AI agents with genuine self-awareness and proactive learning capabilities.

The estimation process of entropy thresholds showcases the clever application of statistical methods in AI systems. The research team employed bootstrap resampling techniques, which estimate statistical parameter confidence intervals through extensive simulations. Simply put, this is akin to repeatedly drawing lots to determine the probability range of an event occurring. The system repeatedly samples from collected data of correct and incorrect steps, training a logistic regression model each time to identify entropy boundaries that distinguish correct from incorrect steps. After 1,000 such repeated experiments, the system calculates a 95% confidence interval, which becomes the dynamic threshold for determining whether guidance should be provided.

This method’s advantage lies in its ability to quantify uncertainty. Unlike fixed threshold settings, dynamic thresholds account for the inherent variability of data, making judgments more reliable. For the 8B model, the threshold range for processing steps is 0.314 to 0.413, while for the final answering steps, it is 0.225 to 0.257. The thresholds for the 32B model are significantly higher, ranging from 0.877 to 1.384 and 0.714 to 0.820, reflecting the greater complexity exhibited by larger models during their reasoning processes.

The probabilistic triggering mechanism design avoids “black-and-white” rigid judgments. When an agent’s entropy falls within the threshold range, the system does not simply decide to provide or withhold guidance; instead, it calculates a trigger probability based on the position of entropy within the range. The closer the entropy is to the upper limit of the range, the higher the trigger probability; the closer it is to the lower limit, the lower the trigger probability. This design simulates the intuitive judgment process humans use in uncertain situations, ensuring that help is available when needed while avoiding excessive frequency of intervention.

The architecture of the experience model embodies the principle of “specialization.” The main agent focuses on task execution, while the experience model is dedicated to analyzing situations and providing guidance. This division of labor prevents a single model from shouldering too many responsibilities, which could lead to performance degradation. The experience model used in experiments is Qwen3-235B-A22B-Instruct-2507. Although this model has a large number of parameters, its task is relatively simple and specific: primarily understanding the current context and generating appropriate guidance suggestions.

Interestingly, even a small model like the 4B experience model can effectively assist the larger 32B primary agent. This finding challenges the conventional notion that “larger models are always better,” indicating that in specific application scenarios, specialized smaller models may be more effective than generalized larger models. It is akin to how a specialist doctor can sometimes provide better expertise than a general practitioner in a hospital.

The organization structure of the experience database employs a thematic hierarchical management approach. The system maintains independent collections of experiences for different types of steps: the processing steps experience database contains 196 experience items divided into 17 themes, while the answering steps experience database contains 190 experience items divided into 11 themes. This classification is not manually predetermined but formed automatically through iterative theme generation algorithms. Whenever new experiences are processed, the system evaluates whether new themes need to be created, existing themes modified, or if new experiences should be classified into current categories.

The mechanism design to prevent excessive intervention reflects a profound understanding of the agents’ learning processes. After the system provides guidance at a certain step, it pauses intervention in the subsequent step, allowing the agent ample time to digest and apply the recently received suggestions. This “guidance-silence-observation” rhythm simulates the teaching style of effective educators, providing necessary assistance while maintaining the learner’s autonomy.

The configuration of the tool environment is also meticulously designed. Agents are equipped with two basic tools: a search tool and an access tool. The search tool utilizes Bright Data’s stable network API service to return relevant website links and summary information. The access tool employs Jina as a web access service and integrates the Qwen3-235B model as a content summarization tool. This combination of tools ensures both the reliability of information retrieval and the necessary information processing capabilities.

The system evaluation adopts the LLM-as-a-Judge method, using a large language model to assess the correctness of answers. While this method may not be as precise as human evaluation, it offers a feasible assessment solution for large-scale experiments. To ensure reliability, each experiment was repeated five times, with the average results serving as the final performance metrics.

8. Vast Prospects for Practical Applications: From the Lab to the Real World

The technological breakthroughs of ExpSeek extend beyond academic research, showcasing application potential that heralds a new developmental phase for AI agents in the real world. This framework of “self-awareness + proactive help-seeking” provides new ideas for addressing many practical issues in current AI applications.

In the customer service sector, ExpSeek can significantly enhance the performance of AI customer service systems. Traditional AI customer service often faces two extremes: either rigidly following pre-set scripts, unable to handle complex or special situations, or being overly “clever” and potentially providing inaccurate or erroneous information. ExpSeek enables AI customer service to recognize its limits and intelligently seek support from expert systems or human agents when faced with questions beyond its pre-set scope, rather than stubbornly providing potentially incorrect answers.

Educational assistance is another promising application direction. In online learning platforms, AI tutors can leverage the principles of ExpSeek to offer more personalized learning guidance. When students encounter difficulties in their studies, AI tutors can not only identify the students’ confusion points but also find the most suitable explanatory methods and practice materials from a vast teaching experience database. More importantly, this guidance is dynamic and targeted rather than standardized and unchanging.

ExpSeek’s value is equally evident in enterprise decision support systems. The complexity and variability of business environments demand that decision support systems possess high adaptability. Traditional decision support systems often rely on preset rules and patterns, making them ill-equipped to respond to new market conditions. In contrast, intelligent decision assistants endowed with ExpSeek capabilities can recognize the novelty and complexity of current situations and proactively invoke relevant historical cases and expert experiences to provide decision-makers with more comprehensive and accurate analysis support.

Medical diagnostic assistance is a particularly noteworthy application scenario. In this field, which demands high levels of accuracy, an AI system’s “self-awareness” becomes especially important. Medical AI based on ExpSeek principles can better recognize its diagnostic confidence levels and proactively seek expert opinions or recommendations for further checks in uncertain situations, rather than issuing vague suggestions that could mislead doctors and patients.

Research assistance is also a promising application direction. In activities such as literature reviews, data analysis, and hypothesis generation, AI assistants must handle a substantial amount of complex and cutting-edge information. ExpSeek enables research AI to better assess its understanding of a particular field or concept and to actively seek relevant expert knowledge or the latest research findings at the boundaries of knowledge, thus providing more reliable and cutting-edge research support.

In the legal consulting realm, ExpSeek’s application value is also pronounced. Legal issues often involve complex text interpretation and case analysis, requiring high levels of professionalism and accuracy. Legal AI equipped with self-awareness can identify the complexity of cases and its own areas of expertise, guiding users to seek help from professional lawyers when addressing questions beyond its capabilities or providing relevant legal texts and similar cases for reference.

It is noteworthy that the application of ExpSeek may also foster new modes of human-machine collaboration. Traditional AI applications often operate on a binary model of “either fully automated or not at all,” whereas ExpSeek introduces an “intelligent hybrid” work model. AI systems can work independently in areas where they excel, seeking assistance from human experts when facing challenges. This model not only maximizes the efficiency of AI but also ensures the effective utilization of human expertise.

From a technological development perspective, ExpSeek also opens new paths for continuous learning and improvement of AI systems. Traditional AI systems, once trained, have fixed capabilities. In contrast, systems with ExpSeek capabilities can continuously refine their experience databases through an ongoing “help-seek-learn” process, achieving genuine and meaningful continuous learning and self-improvement.

Of course, the widespread application of ExpSeek does face some challenges and considerations. First is the efficiency issue; frequent experience queries and guidance generation may impact the system’s response speed. Next is the need to ensure the quality of experiences, as erroneous or biased experiences could mislead AI systems’ judgments. There are also privacy and security considerations, especially in sensitive areas of application, where it is crucial to ensure that the experience database does not contain sensitive information.

Despite these challenges, the technological direction represented by ExpSeek remains highly promising. With advancements in computational power and further optimization of algorithms, these technical challenges are likely to be effectively addressed. More importantly, ExpSeek points the way toward building smarter, more reliable, and human-centered AI systems, which is significant for promoting the broad application of AI technology in the real world.

Ultimately, the true value of ExpSeek lies in making AI systems more “humble” and “wise.” An AI system that knows its limits and actively seeks help is far more reliable than one that mistakenly believes it knows everything. This design philosophy not only enhances system performance but also strengthens trust in AI systems, laying the foundation for the healthy development of AI technology.

Q&A

Q1: What is the core innovation of the ExpSeek framework?
A: The core innovation of ExpSeek is enabling AI agents to learn “self-awareness” and “proactive help-seeking.” It determines when help is needed by monitoring the agent’s internal uncertainty (entropy) and seeks the most relevant guidance from a structured experience database. This transforms the traditional passive experience acceptance model, allowing agents to actively seek suitable assistance at every step of task execution.

Q2: What performance improvements did ExpSeek achieve in experiments?
A: In four challenging network agent benchmark tests, ExpSeek achieved an average improvement of 9.3% on the 8B model and 7.5% on the 32B model. Most impressively, even using a small 4B model as the experience guiding model, significant performance improvements were observed in the larger 32B model, demonstrating the feasibility of “weaker guiding stronger” in the AI field.

Q3: How does ExpSeek avoid over-intervention in agents?
A: ExpSeek employs multiple mechanisms to prevent over-intervention. First, it uses a probability-triggered mechanism based on entropy, providing assistance only when agents genuinely feel confused. Secondly, after guidance is provided, the system pauses intervention for the next step, giving agents time to digest the suggestions. Lastly, the guidance content is expressed heuristically, not directly telling agents what to do but instead guiding them to think in the right direction.