HoosierHelp: Benchmarking LLM Agents for Social Service Navigation

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "HoosierHelp: Benchmarking LLM Agents for Social Service Navigation".

Jane: The paper was written by Yiyang Li, Weixiang Sun, Tianyi Ma, Kaiwen Shi, Zheyuan Zhang et al. from University of Notre Dame.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: Welcome back to the show, everyone. We’ve got a fascinating paper on the table today, and it’s called "HoosierHelp: Benchmarking LLM Agents for Social Service Navigation." Jane, I have to say, the title alone got me curious—this isn’t your typical AI benchmark.

Jane: Absolutely, Tom. And that’s exactly why I wanted to dig into it. When we hear "benchmark," we usually think of math problems or coding challenges. But this one is about something much more human—helping people find food, housing, utility assistance, that kind of thing. It’s built on real resources from Indiana, over three thousand nine hundred of them, covering twenty-one different service categories.

Tom: Right, and that’s what makes it so different. The authors took the Indiana two hundred eleven directory—that’s the free hotline people call to get connected with local help—and turned it into a structured database. Then they built an interactive test where an AI agent has to chat with a simulated person in need, ask the right questions, search the database, and recommend the right resource.

Jane: And the clever part is how they made the simulated users feel real. They didn’t just have everyone answer politely and directly. Some users are impatient, some ramble, some make requests the agent can’t actually do, like "can you just pay my bill for me?" And some even contradict themselves. That’s where the benchmark gets really interesting.

Tom: So it’s not just about whether the AI can call a tool correctly. It’s about whether it can handle a messy, real-world conversation where the person on the other end isn’t always cooperative.

Jane: Exactly. And the results show that even the best models, like GPT-five point four and Qwen3 point 7-Max, really struggle. The best model only got about sixty-seven percent of the final recommendations exactly right. And when users were self-contradictory, that number dropped to near zero for some models. It’s a sobering reminder that we’re still far from having reliable AI assistants for this kind of high-stakes navigation.

Tom: So the title "HoosierHelp" isn’t just a catchy name—it’s a real test of whether AI can be trusted with something as important as connecting people to basic needs.

Jane: And that’s the big implication. If we want AI to help with social services, we need to know where it fails. This paper gives us a map of exactly that. And I think the next thing we need to talk about is how they actually built this benchmark and what the agents had to do.

Tom: Good point. Let’s get into the details of the benchmark construction next.

Summary: Tom: So we’ve set the stage with the title, but now let’s get into the meat of "HoosierHelp: Benchmarking LLM Agents for Social Service Navigation." Jane, how did they actually set up these conversations?

Jane: So each episode starts with a hidden profile. The agent doesn’t see it, but it contains the user’s real need, their constraints, and the target resource they should end up with. The agent has to figure all of that out through conversation. And there are two tools the agent can use—one to search the database, and one to make a final recommendation.

Tom: And the search tool is pretty detailed, right? It’s not just "find me food." It has fields for service category, location, schedule, intake method, documents, and eligibility.

Jane: Exactly. And that’s where the grounding problem shows up. The paper found a big gap between what the agents searched for and what they finally recommended. The best model had a tool exact-match score of only thirty-eight point seven five percent, but a resource exact-match score of sixty-seven point five percent. That means the agents were often picking the right resource without faithfully preserving all the user’s constraints in the search.

Tom: That sounds like a recipe for trouble. If the agent doesn’t search with the right schedule or the right documents, it might recommend something the user can’t actually use.

Jane: Right. And the paper shows this clearly when they split the results by constraint satisfiability. When the user’s first choice directly matched a resource, models did pretty well. But when the first choice had no match and the user had to fall back to an alternative, performance dropped dramatically. For example, Qwen3 point 6-35B-A3B went from eighty-five percent down to thirty-eight percent on resource accuracy.

Tom: So the agents are bad at handling the "no results" case. They either give up, or they broaden the search too much and lose track of what the user actually said.

Jane: Precisely. And the user behavior split makes it even worse. Self-contradictory users were the hardest condition by far. Tool exact match fell below fifteen percent for every single model. That tells us the agents aren’t recognizing a contradiction as something they need to clarify before searching.

Tom: So they just take the last thing the user said and run with it?

Jane: Often, yes. Or they take the first thing and ignore the correction. Either way, they’re not treating the conversation as a source of facts that need to be reconciled.

Tom: And what about the interaction length? Did longer conversations help?

Jane: Not necessarily. The paper found that more turns didn’t reliably improve accuracy. Some models, like Qwen3 point 6-35B-A3B, averaged only four turns and had low tool accuracy. Others, like GPT-five point four, took over six turns and did better. But even the longer conversations didn’t close the gap on the hard cases.

Tom: So it’s not about talking more—it’s about asking the right questions and keeping track of the answers.

Jane: Exactly. And that brings us to what the paper suggests we should do about it.

Improvements: Tom: Alright, so we know the problem. What does "HoosierHelp: Benchmarking LLM Agents for Social Service Navigation" suggest we do about it? Jane, what are the key improvements the authors are pushing for?

Jane: The main call is for agents that are more robust to complex and non-ideal user interactions. Specifically, they highlight the need for better information elicitation and grounded tool use. That means agents need to actively ask for missing constraints, not guess them, and they need to keep those constraints consistent across the whole conversation.

Tom: So it’s about memory and attention. The agent has to remember what the user said in turn two when it’s making a search in turn six.

Jane: Right. And the paper shows that this is exactly where models fail. For example, in one of the diagnostic cases they shared, a user gave two needs—public safety and food assistance—with different schedules. The agent handled the first need perfectly but completely forgot about the food assistance. It just finalized with one recommendation.

Tom: So it’s not just about handling one complex conversation—it’s about handling multiple threads at once.

Jane: Exactly. And another improvement they suggest is better fallback reasoning. When a search returns no results, the agent should ask the user which constraint can be relaxed, rather than just broadening everything on its own. In one case, the agent expanded the location from a specific ZIP code to a whole county without asking, and ended up recommending the wrong resource.

Tom: That’s a classic case of the agent trying to be helpful but actually making things worse.

Jane: Yes. And the third improvement is about recognizing non-actionable requests. When a user says "can you just pay my bill," the agent needs to redirect that into the real need—utility assistance—without getting distracted. Some models handled this okay, but many lost the thread and recommended something irrelevant.

Tom: So the improvements are really about three things: asking the right questions, handling empty results intelligently, and staying focused on the real need.

Jane: And the paper suggests that current agents lack robust mechanisms for all three. They’re good at pattern-matching from the conversation to a plausible resource, but they’re not good at maintaining a faithful requirement state.

Tom: So the fix isn’t just a better prompt or a bigger model—it’s a different architecture or training approach that emphasizes grounding and consistency.

Jane: Exactly. And I think that’s a really important message for the field. We can’t just throw more compute at this problem. We need agents that are designed to be careful, not just clever.

Tom: That’s a great point. Let’s bring in Lu and Meng to get their take on the practical side of these improvements.

Conclusion: Tom: We’ve covered a lot of ground on "HoosierHelp: Benchmarking LLM Agents for Social Service Navigation." Jane, before we wrap up, let’s pull it all together.

Jane: Sure. The paper gives us a realistic, interactive benchmark for a high-stakes domain. It shows that current LLM agents are unreliable for social service navigation, especially when users are impatient, contradictory, or have constraints that don’t match anything at first. The best model only got about two-thirds of the final recommendations right, and that’s not good enough for something this important.

Tom: And the key takeaway is that we need agents that are better at eliciting information, handling fallback scenarios, and staying grounded in what the user actually said.

Jane: Right. It’s not about making the agent faster or more conversational. It’s about making it more careful and more consistent. The paper’s diagnostic failures show that even small lapses in memory or attention can lead to wrong referrals.

Tom: So what’s the bigger picture here? What does this mean for the world?

Jane: It means we’re not ready to deploy AI agents for social service navigation without human oversight. But it also gives us a clear roadmap for what needs to improve. If we can build agents that pass benchmarks like this, we could help millions of people access the resources they need, around the clock, without overburdening human specialists.

Tom: That’s the promise. And this paper is a crucial step toward making it real.

Jane: Absolutely. It’s a benchmark that measures something that actually matters. And for that, I think we should give the authors a lot of credit.

Tom: Agreed. So we’ll say goodbye to "HoosierHelp" and get ready to look at the next paper on the arXiv. Thanks for listening, everyone.

Jane: See you next time.