AI search agents don't fail at searching, they fail at asking the right questions when queries get ambiguous

But real-world queries are often vague, incomplete, or flat-out wrong. In long reasoning chains, every unresolved ambiguity compounds and steers the agent down the wrong path. If the model picks the wrong entity at an early node, it keeps searching with clean syntax but misses the actual target entirely.

Four types of ambiguity

DiscoBench contains 211 tasks with a total of 463 ambiguous points across eleven knowledge domains, including video games, sports, music, film, science, and politics. Each task is split into multiple checkpoints. At each checkpoint, the agent can choose between three actions: keep searching, ask the user for clarification, or give an answer.

The researchers define four types of ambiguity. A description might match multiple entities, apply to different time periods or versions, allow for multiple valid ranking or evaluation criteria, or contain an outright factual error. The dataset is mostly written in Chinese to reflect typical search patterns on the Chinese-language web.

When the agent asks a useful follow-up question, an LLM-based user simulator releases a predefined clue that helps narrow the search. All search queries run through the agent search engine Tavily, and Gemini 3 Flash serves as the simulator.

Even large models stay below 50 percent

The team tested eleven models released in the past six months, including Claude Opus 4.7, GPT 5.4, Gemini 3.1 Pro Preview, Doubao Seed 2.0 Pro, DeepSeek V4 Pro, Kimi K2.6, GLM 5.1, Qwen3.6 Max, MiniMax M2.7, MiMo v2.5 Pro, and Hunyuan 3.0 Preview.

Without an explicit hint about possible ambiguity, Doubao Seed 2.0 Pro hit the highest end-to-end accuracy at 43.1 percent. Gemini 3.1 Pro followed at 40.8 percent, Claude Opus 4.7 at 39.8 percent. Weaker models like MiniMax M2.7 and Qwen3.6 Max managed only 16.1 and 12.3 percent, respectively.

There's a gap between individual step scores and overall results. Claude Opus 4.7, for example, solves 57 percent of checkpoints correctly but only reaches 39.8 percent end-to-end. The individual research steps work fine on their own, but a single unresolved ambiguity is enough to collapse the entire chain.

A warning prompt isn't enough

The authors also tested what happens when the system prompt explicitly tells the agent to watch for ambiguity and ask a follow-up question when in doubt. This "Guided" mode was meant to show the ceiling that's reachable when models don't have to figure out on their own that a question is underspecified.

Averaged across ten models, end-to-end accuracy rose from 28.6 to 33.7 percent. Detection F1 jumped much more sharply, from 45.3 to 64.9 percent. The hint mostly helped models spot ambiguity without actually helping them finish the research successfully. For Claude Opus 4.7, end-to-end accuracy even dipped slightly under the guided prompt, despite a higher checkpoint pass rate.

Searching more is worse than guessing

The behavioral profile analysis breaks down what agents actually do at ambiguous checkpoints. Models that search first and then ask a follow-up ("SearchThenAsk") average a 93.4 percent success rate. Guessing without asking ("DirectGuess") drops to 56.5 percent. Models that search repeatedly but still guess instead of asking ("SearchHeavyGuess") do even worse at 51.9 percent. According to the authors, the repeated searches suggest the model already spotted the ambiguity but never turned it into a user interaction.

That pattern also explains why more tool calls don't lead to better results. Claude Opus 4.7 searches more often than most other models but still trails Gemini 3.1 Pro and Doubao Seed 2.0 Pro in accuracy. Searching harder doesn't help if the agent never asks the right question.

Spotting ambiguity and asking good questions are two different skills

Detection ability and question quality don't track together. Qwen3.6 Max only reaches a Detection F1 of 16 percent and asks an average of 0.07 follow-up questions per task in the neutral setting. When it does ask, though, 94.7 percent of its questions are factually correct and 89.5 percent lead to progress. MiniMax M2.7 asks far more often but only achieves a follow-through rate of 60.7 to 66.5 percent.

A useful research agent needs both skills: recognizing when to ask a follow-up question and framing it so the answer actually moves the search forward.

Broken down by ambiguity type, factual errors are easiest to detect because they create direct contradictions during research. Entity and criteria ambiguities are harder because multiple plausible candidates or unclear evaluation standards can coexist without any obvious contradiction.

AI agents need better follow-up strategies

Without access to search tools, the tested models collapse. Doubao Seed 2.0 Pro drops from 43.1 to 2.4 percent, Gemini 3.1 Pro from 40.8 to 19.9 percent. DiscoBench can't be solved from stored model knowledge alone. At the same time, models perform much better when ambiguity is removed from the questions, with accuracy jumping by 26.8 to 40.2 points depending on the model. The authors conclude that future search agents need mechanisms that turn search uncertainty into user interaction, on top of their retrieval and reasoning abilities.

Other recent work confirms that current search agents have basic weaknesses in how they research. One study found that leading models on benchmarks like BrowseComp often just confirm what they already know. On the purpose-built LiveBrowseComp with facts beyond the knowledge cutoff, all systems dropped by 25 to 40 points. The Halluhard benchmark also showed that Claude Opus 4.5 with web search hallucinates in about 30 percent of cases, mainly when verifying the content of cited sources.

Anthropic tackled this problem in its latest model update, Claude Opus 4.8. The model is supposed to flag uncertainties more often and leaves bugs in its own code uncommented about four times less frequently than its predecessor. Perplexity is trying a different approach with Search as Code, letting models write their search workflows as Python programs instead of calling a prebuilt API.